{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten,Reshape\n",
    "from keras.layers import Conv1D, MaxPooling1D\n",
    "from keras.utils import np_utils\n",
    "from keras.layers import LSTM, LeakyReLU, CuDNNLSTM\n",
    "from keras.callbacks import CSVLogger, ModelCheckpoint, EarlyStopping\n",
    "import h5py\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "from keras import regularizers\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '1'\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL']='2'\n",
    "\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "set_session(tf.Session(config=config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File(''.join(['data/allcoin2015to2017_wf.h5']), 'r') as hf:\n",
    "    test_inputs = hf['test_inputs'].value\n",
    "    test_outputs = hf['test_outputs'].value\n",
    "    test_input_times = hf['test_input_times'].value\n",
    "    test_output_times = hf['test_output_times'].value\n",
    "    original_test_inputs = hf['original_test_inputs'].value\n",
    "    original_test_outputs = hf['original_test_outputs'].value\n",
    "    original_datas= hf['original_datas'].value\n",
    "    inputs = hf['inputs'].value\n",
    "    outputs = hf['outputs'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "step_size = test_inputs.shape[1]\n",
    "units= 50\n",
    "second_units = 30\n",
    "batch_size = 8\n",
    "nb_features = test_inputs.shape[2]\n",
    "epochs = 50\n",
    "output_size=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.000463</td>\n",
       "      <td>0.000508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.000251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.000181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000876</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000606</td>\n",
       "      <td>0.000852</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.000936</td>\n",
       "      <td>0.000982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.005550</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.005369</td>\n",
       "      <td>0.005514</td>\n",
       "      <td>0.005558</td>\n",
       "      <td>0.005602</td>\n",
       "      <td>0.005712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.000924</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000625</td>\n",
       "      <td>0.000836</td>\n",
       "      <td>0.000915</td>\n",
       "      <td>0.000998</td>\n",
       "      <td>0.001337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.000603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000193</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.000276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.002739</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.002678</td>\n",
       "      <td>0.002691</td>\n",
       "      <td>0.002709</td>\n",
       "      <td>0.002786</td>\n",
       "      <td>0.002866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.001064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.003449</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.003365</td>\n",
       "      <td>0.003431</td>\n",
       "      <td>0.003559</td>\n",
       "      <td>0.003720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.001709</td>\n",
       "      <td>0.000336</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>0.001433</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>0.001903</td>\n",
       "      <td>0.002452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.002777</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.002650</td>\n",
       "      <td>0.002691</td>\n",
       "      <td>0.002768</td>\n",
       "      <td>0.002852</td>\n",
       "      <td>0.002937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.034560</td>\n",
       "      <td>0.015226</td>\n",
       "      <td>0.027245</td>\n",
       "      <td>0.028981</td>\n",
       "      <td>0.030992</td>\n",
       "      <td>0.032183</td>\n",
       "      <td>0.091036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.002028</td>\n",
       "      <td>0.001116</td>\n",
       "      <td>0.001052</td>\n",
       "      <td>0.001318</td>\n",
       "      <td>0.001601</td>\n",
       "      <td>0.002338</td>\n",
       "      <td>0.004920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000774</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>0.000923</td>\n",
       "      <td>0.001066</td>\n",
       "      <td>0.001548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.003410</td>\n",
       "      <td>0.001817</td>\n",
       "      <td>0.001400</td>\n",
       "      <td>0.002647</td>\n",
       "      <td>0.003115</td>\n",
       "      <td>0.003802</td>\n",
       "      <td>0.008961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13.0</td>\n",
       "      <td>0.004266</td>\n",
       "      <td>0.001962</td>\n",
       "      <td>0.002524</td>\n",
       "      <td>0.003162</td>\n",
       "      <td>0.003526</td>\n",
       "      <td>0.004396</td>\n",
       "      <td>0.009780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.003336</td>\n",
       "      <td>0.001229</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.002349</td>\n",
       "      <td>0.003114</td>\n",
       "      <td>0.003937</td>\n",
       "      <td>0.005883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.006197</td>\n",
       "      <td>0.003059</td>\n",
       "      <td>0.003984</td>\n",
       "      <td>0.004132</td>\n",
       "      <td>0.004717</td>\n",
       "      <td>0.007506</td>\n",
       "      <td>0.016080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count      mean       std       min       25%       50%       75%  \\\n",
       "partition                                                                      \n",
       "0           23.0  0.000427  0.000049  0.000315  0.000401  0.000412  0.000463   \n",
       "1           11.0  0.000020  0.000007  0.000014  0.000016  0.000018  0.000021   \n",
       "2           17.0  0.000105  0.000040  0.000049  0.000083  0.000098  0.000124   \n",
       "3           40.0  0.000049  0.000055  0.000006  0.000013  0.000032  0.000056   \n",
       "4           27.0  0.000012  0.000007  0.000003  0.000008  0.000010  0.000015   \n",
       "5           11.0  0.000135  0.000045  0.000011  0.000134  0.000140  0.000160   \n",
       "6           11.0  0.000876  0.000105  0.000606  0.000852  0.000917  0.000936   \n",
       "7           11.0  0.005550  0.000099  0.005369  0.005514  0.005558  0.005602   \n",
       "8           11.0  0.000028  0.000012  0.000018  0.000020  0.000023  0.000030   \n",
       "9           41.0  0.000924  0.000156  0.000625  0.000836  0.000915  0.000998   \n",
       "10          37.0  0.000101  0.000150  0.000004  0.000019  0.000031  0.000108   \n",
       "11          24.0  0.000012  0.000017  0.000002  0.000004  0.000005  0.000011   \n",
       "12          11.0  0.000405  0.000119  0.000097  0.000395  0.000415  0.000441   \n",
       "13          16.0  0.000228  0.000023  0.000193  0.000208  0.000228  0.000240   \n",
       "14          15.0  0.002739  0.000061  0.002678  0.002691  0.002709  0.002786   \n",
       "15          41.0  0.000234  0.000143  0.000112  0.000178  0.000204  0.000245   \n",
       "16          18.0  0.003449  0.000162  0.003099  0.003365  0.003431  0.003559   \n",
       "17          50.0  0.001709  0.000336  0.000969  0.001433  0.001732  0.001903   \n",
       "18          16.0  0.002777  0.000093  0.002650  0.002691  0.002768  0.002852   \n",
       "19          16.0  0.034560  0.015226  0.027245  0.028981  0.030992  0.032183   \n",
       "20          23.0  0.002028  0.001116  0.001052  0.001318  0.001601  0.002338   \n",
       "21          23.0  0.000985  0.000214  0.000774  0.000824  0.000923  0.001066   \n",
       "22          17.0  0.003410  0.001817  0.001400  0.002647  0.003115  0.003802   \n",
       "23          13.0  0.004266  0.001962  0.002524  0.003162  0.003526  0.004396   \n",
       "24          12.0  0.003336  0.001229  0.001894  0.002349  0.003114  0.003937   \n",
       "25          25.0  0.006197  0.003059  0.003984  0.004132  0.004717  0.007506   \n",
       "\n",
       "                max  \n",
       "partition            \n",
       "0          0.000508  \n",
       "1          0.000034  \n",
       "2          0.000198  \n",
       "3          0.000251  \n",
       "4          0.000030  \n",
       "5          0.000181  \n",
       "6          0.000982  \n",
       "7          0.005712  \n",
       "8          0.000057  \n",
       "9          0.001337  \n",
       "10         0.000677  \n",
       "11         0.000072  \n",
       "12         0.000603  \n",
       "13         0.000276  \n",
       "14         0.002866  \n",
       "15         0.001064  \n",
       "16         0.003720  \n",
       "17         0.002452  \n",
       "18         0.002937  \n",
       "19         0.091036  \n",
       "20         0.004920  \n",
       "21         0.001548  \n",
       "22         0.008961  \n",
       "23         0.009780  \n",
       "24         0.005883  \n",
       "25         0.016080  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_file = 'allcoin2015to2017_WF_CNN_2.csv'\n",
    "df = pd.read_csv(result_file)\n",
    "zero_indexes = [i for i,e in enumerate(df.epoch) if e==0]\n",
    "zero_indexes\n",
    "df['partition'] = -1\n",
    "for z in zero_indexes:\n",
    "    df.partition += [0]*(z) + [1] * (len(df)-z )\n",
    "\n",
    "df.loc[df.val_loss == df.val_loss.min(), :]\n",
    "df.groupby('partition').describe()['val_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "allcoin2015to2017_WF_CNN_2.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "file_start= 'allcoin2015to2017_WF_CNN_2'\n",
    "for root, dirs, files in os.walk(\".\"):  \n",
    "    for filename in files:\n",
    "        if filename[:len(file_start)] == file_start and 'csv' in filename:\n",
    "            print(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "    # 2 Layers\n",
    "model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=3, filters=8, kernel_size=20))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Conv1D( strides=4, filters=1, kernel_size=16))\n",
    "\n",
    "'''\n",
    "# 3 Layers\n",
    "model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=3, filters=8, kernel_size=8))\n",
    "#model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D(activation='relu', strides=2, filters=8, kernel_size=8))\n",
    "#model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D( strides=2, filters=nb_features, kernel_size=8))\n",
    "\n",
    "\n",
    "# 4 layers\n",
    "model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=2, filters=8, kernel_size=2))\n",
    "#model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D(activation='relu', strides=2, filters=8, kernel_size=2))\n",
    "#model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D(activation='relu', strides=2, filters=8, kernel_size=2))\n",
    "#model.add(LeakyReLU())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv1D( strides=2, filters=nb_features, kernel_size=2))\n",
    "'''\n",
    "model.load_weights('weights/allcoin2015to2017_WF_CNN_2partition_25.hdf5')\n",
    "model.compile(loss='mse', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted = model.predict(test_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(original_datas)\n",
    "predicted_inverted = []\n",
    "for i in range(len(predicted)):\n",
    "    predicted_inverted.append(scaler.inverse_transform(predicted[i,:]))\n",
    "predicted_inverted = np.array(predicted_inverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 5.08845753,  5.79730619,  3.63272746,  4.3039661 ],\n",
       "        [ 5.09582317,  5.84995571,  3.63342788,  4.28445653],\n",
       "        [ 5.07669878,  5.80768391,  3.65259413,  4.28445654],\n",
       "        ..., \n",
       "        [ 5.5370005 ,  6.41386149,  3.81620064,  4.74807096],\n",
       "        [ 5.5509778 ,  6.33608138,  3.82237539,  4.75626852],\n",
       "        [ 5.55281248,  6.30782847,  3.82854706,  4.75843819]],\n",
       "\n",
       "       [[ 5.09582317,  5.84995571,  3.63342788,  4.28445653],\n",
       "        [ 5.07669878,  5.80768391,  3.65259413,  4.28445654],\n",
       "        [ 5.08845753,  5.84699669,  3.67973854,  4.32521475],\n",
       "        ..., \n",
       "        [ 5.5509778 ,  6.33608138,  3.82237539,  4.75626852],\n",
       "        [ 5.55281248,  6.30782847,  3.82854706,  4.75843819],\n",
       "        [ 5.54320545,  6.33608138,  3.83163752,  4.75843819]],\n",
       "\n",
       "       [[ 5.07669878,  5.80768391,  3.65259413,  4.28445654],\n",
       "        [ 5.08845753,  5.84699669,  3.67973854,  4.32521475],\n",
       "        [ 5.08426232,  5.80768391,  3.65928955,  4.29590066],\n",
       "        ..., \n",
       "        [ 5.55281248,  6.30782847,  3.82854706,  4.75843819],\n",
       "        [ 5.54320545,  6.33608138,  3.83163752,  4.75843819],\n",
       "        [ 5.56017844,  6.35721728,  3.83221227,  4.78442008]],\n",
       "\n",
       "       ..., \n",
       "       [[ 4.2942499 ,  4.87770437,  4.82886051,  5.15741425],\n",
       "        [ 4.30001431,  4.86713642,  4.83503526,  5.08698407],\n",
       "        [ 4.30490202,  4.87770437,  4.83441778,  5.13428202],\n",
       "        ..., \n",
       "        [ 4.14437524,  4.50359896,  5.05918303,  5.22370124],\n",
       "        [ 4.14830731,  4.52473486,  5.12495071,  5.31327636],\n",
       "        [ 4.13668936,  4.50571255,  5.09128761,  5.27816457]],\n",
       "\n",
       "       [[ 4.30001431,  4.86713642,  4.83503526,  5.08698407],\n",
       "        [ 4.30490202,  4.87770437,  4.83441778,  5.13428202],\n",
       "        [ 4.30129529,  4.87981795,  4.81828007,  5.13428202],\n",
       "        ..., \n",
       "        [ 4.14830731,  4.52473486,  5.12495071,  5.31327636],\n",
       "        [ 4.13668936,  4.50571255,  5.09128761,  5.27816457],\n",
       "        [ 4.15013965,  4.50571255,  5.10777421,  5.22576105]],\n",
       "\n",
       "       [[ 4.30490202,  4.87770437,  4.83441778,  5.13428202],\n",
       "        [ 4.30129529,  4.87981795,  4.81828007,  5.13428202],\n",
       "        [ 4.26962306,  4.83995736,  4.816511  ,  5.13428202],\n",
       "        ..., \n",
       "        [ 4.13668936,  4.50571255,  5.09128761,  5.27816457],\n",
       "        [ 4.15013965,  4.50571255,  5.10777421,  5.22576105],\n",
       "        [ 4.14213352,  4.51416691,  5.1077742 ,  5.32572154]]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-16 08:55:00</td>\n",
       "      <td>17860.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-16 09:00:00</td>\n",
       "      <td>17883.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-16 09:05:00</td>\n",
       "      <td>17823.282001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-16 09:10:00</td>\n",
       "      <td>17860.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-16 09:15:00</td>\n",
       "      <td>17846.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                times         value\n",
       "0 2017-12-16 08:55:00  17860.000001\n",
       "1 2017-12-16 09:00:00  17883.000000\n",
       "2 2017-12-16 09:05:00  17823.282001\n",
       "3 2017-12-16 09:10:00  17860.000000\n",
       "4 2017-12-16 09:15:00  17846.900000"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_df = pd.DataFrame()\n",
    "ground_true_df['times'] = pd.to_datetime(test_input_times[:,:,0].reshape(-1),unit='s')\n",
    "ground_true_df['value'] = original_test_inputs[:,:,0].reshape(-1)\n",
    "ground_true_df.set_index('times')\n",
    "ground_true_df = ground_true_df.drop_duplicates(subset=['times','value'])\n",
    "ground_true_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LXf36wzG7jy44lzGDiqhPNrUXrKu7CQ5kJ9/qrWJnwIfmrR3QQIyIiIiIiIhoqGMwNcyl\ncza+dMBE/OpL+5a8JqJ1HUw9+cEG/3hja8Y/ruzjVj5VVC+8p4wSsv3k0Q+7fP7f31zV5/dUigz/\ntnRk8b8vLR2w9yUiIiIiIiIa6hhMDXMZ00YyFim6857UXTD1mekN/vELizb5x5V93MqnKlbFpQZT\n9ZUx/3jp5o5+u49tkVLmeG1sy3RxJRERERERERGpGEwNc2nTRjKq+217AHDv+Yfi+e8f6T+WIdDp\nMybgihOmAQCqlTa9v5xzsH/86Hvr/OOKWP9VTGlKWLbHuBoAwPLGTv/cZ/ce6x9/9c9vAAAO2Kk2\n9BruAA1ADwVmFbEuriQiIiIiIiIiFYOpYcx1XSWYCoKe/XeqxVRvRz4gqJj61K4jcfExUzGxLol2\nb7j4xUfvBj2i4Q9f27/g9ftjxtR935oZCs0A+GHZDx6a759TZzm1psVQ9GRMx0E71/nnU13sSNiX\n1PfpHKD3JCIiIiIiIhoOGEwNY1nLgesCiZgeauXL301PFlPJsCcRDSqhvnn4LgCAU/YbH3rO7mOr\nQ9f1lU/tOjIUmgFAbUXUP3YcF2hpgaUEU/LYtF0YuoaqbAoRx4ZdqmIqnQYyXbTcbd4MfNj1DKvQ\ny3lhVFXcwPqW9DY/j4iIiIiIiGhHx2BqGJMtZsmojphe+q9atvLJICeuhFhJL3zSNA2XHTfVP//D\nE6f3+f2WMiIZBFN/+8VfgLo67Pb+GwXXWbaDOBx8+Pv/wtWzby+9Q15VFbDLLqXf8MYbgcMO2+b7\na0nlAAD7T6rFKm+HPiIiIiIiIiLqHoOpYSytBFORIsPEJdnKl18xpWlAIhr8ikysq/CPZfvcQFBb\nBq2XXwEA7LLondA1K7d0wnJcVGdFMHTG+8/CLhVMOQ6wcWPpN6ytBdrbAcvq9t5c18XN/yd24qtJ\nGsjZTrfPISIiIiIiIiKBwdQw9rPHRDtaMqajJVU6SPIrppxwxVQyqoeGkE+qS/rHJ+07rs/vFwCQ\nSolWOwBff+8pXPrqvYjqERy49iMkzAxsL2tyHBdj27bg7HeeAACcd9XfMWLLRlRnxYB0DS7sXE5U\nP3V2iva8+fML3++uu4BPPgGyWeDll8W5Wm+Ieltbt7e7tTPnH8f0CHJW/wdTc1c04Zp/L+z39yEi\nIiIiIiLqb30/vZoGjRcWbQYggqbPTG8AAMz64j4F18mKKRn6bGgV85fyh4fvO7EW1XEDM3auQ9zo\npx35zj0XWLAAWLQIxy6di4bOZox0snj4nz/Cit32wSNj9xXXuS7+/th1mLphGZ6efjhm3/ldAMAN\n1//Tf6n4P/4O/PjHIuh69VXgxRdFJZRkWcA3vwmMHAlcey1w8cXAo48CI0aI9ZYWoL6+y9vd0pEN\n3s/QkbX6b/h5xrThusB/3zUXnTkblx47FXWV3AWQiIiIiIiIhi4GU8OUGpB8asooJKI6Vs46qei1\n1QnxayAHpK/Y0ln0umRMx4JrT+zjO81jmkBMhC316VZsraiF5lUu7bJ0Adxx+wEAHBcY17xJ3Lcd\nVIMlrOD4zw+/hR8BohrqxRfFyU2bgvfaskX83LoVWL5cHC9ZAuy0kzjuakC6py0dtPvFjP6tmDr4\nFy/AdBxkTPEe61vTDKaIiIiIiIhoSGMr3zDVmQ2CqZpk1/njRUfvhsuPn4avHTwJAGB0MY+q3+Vy\nfjBVl25Hc7Ia6Ojwl12Ie9McG1FbtNElzSBAijkiKNIdB3EZUiUSweu3tATHW7cG60rLorx+4XIl\nxCqhPRMEYTEjguaUiVVbiwd7vdWetfxQCgh2AyQiIiIiIiIaqhhMDVMdGRHQ7DW+JjQnqphEVMcl\nx05F1Nu5z9DLHExFxS58FVYW6WgiFEwZjghjXMcBvJCqMpf210caIriJwIUrP4arDEFvbg6OOzuD\ndfkdWRY+ahKB18/ufwd45hlg1qySt9vmBVNPXnK4//2d8sfXtv3z9kJ+qyURERERERHRUMNgapjq\nyIpg6nvH7Lbdz5UBS1korXyjIjZO+/TUUDAVlRVRZlCpJHfiA4CJepGd9NRgSq2YksPNXRewvZCn\nsxNPfNIEAIjbJvDcc8ANN5S83XYvABxTk8DpMyaIexugirNUrvSugeta0vi/j7uv+CIiIiIiIiIq\nJwZTw5QMpqri0e1+blmDKaWVT0ulUFFb7c962nzTLTBs8bkMM4uIVz2ltvLtVR/MXIp614ba9NS5\nUa2tAAA3GsWyK68V50wT0yaPAgDELa96KycqqH740Hz86cWlodttS4uArDphYPrYapy6/3hUxQdm\ndFvaLF0xdfItr+Kbf5s3IPdBRERERERE1FMMpoapjqwITKoS2x+SmLZoh/vmp3fp03vatjc3RRi0\nebPYNc80/TDJ2v8AxLywKZLJ+MHTLz+3q//0+kgwg+n8tx8DAGRSShilBlN33CHeMqLj2JvmwK6s\nBCwLHd6eAHE7h/mbUn4w9dA7a/HrZz/xn/6PN1fhN88tFtd6uxRWxg2sbkqhNR1UdPWVaF6LpWW7\nJa4EmjrFPbtu6WuIiIiIiIiIyo3B1BD20ieb/RApX4c3/Lwqrm/368pqq4t70Aa4ze68E/j5zwvP\ny4qpl14Sjxsa/DApkkz4rXzRVNDeV+cqIZASPMW93foWrGgM1tPBPCocfTQAoLW6DgBgRQxY2RyW\nNGcBAIZt4+OmbLjVT3HrS8sKzj29YEPJtd5qqIqHHjvbEDpl+3GXQCIiIiIiIqLeYjA1RL25fCvO\nvettTP3J01je2FGwLoef96SVryYhnjMiuf3P3WbPPw88+GDheRlMbfLmI517rh826RWVfitfNBXs\nfKelghlTyGb9w4wh2voqoYQzXvUTAOCrXwW+8AXk4kkAgGsYeGXRBryyQsyhMhwLnd4EdVd5XSlu\nFP7zuehoEeY1VMcL1norl1chtWRTByZf+STmrmgKnVfDypx3vLE1g8b2ws9AREREREREVE4Mpoao\n5s4gYLnrtZUF671p5XvkwsPwm6/s179DvC3L330Pd9wBXH21qEzavBmorRUtfIAIqbwqJ60igVEp\nb3i5Mvw8VAW1eTMAIKtH/RBLs5Rrm5QQxxu0HrHEdTlNx8Yt7TAjosos6thIVIjQysoqgZYnViSY\nkgPQjX747izHwRf2HYeHv3sYAOCeN1YBAP7rz2+Erlu5JQjtbntpGZY1dmDmjbNx8PUv9Pk9ERER\nEREREfUGg6khSg2NmlOFoUlH1oamARXR7W/l27WhCl8+cGKv7q9bpgkYXmj25JPAo48C7e0iOJo6\nVQRXgAivvF359MpKTGoRlVQRW9mRrr09OF4lwpqUEYfhimohW50rtXFjcOyFY4bX8pd2NUQdG7YX\nTO1aF0ej1xJppYNqo1xee5w6W10GgbIdsi+ZloMxNQnsMqpS3IdSGeU4QTXVciWY+t+XluHY377c\n5/dCRERERERE1BcYTA1RhjIIu9gQ7I6MhcqYgUh/Vj31hmUFwVRzM1BfD7R41VD19UFFlGGIdV2H\nXluDmqwIXWLqzKe2tuDY22nPcIJgyFVb/TqUtkevYsrwXivjRqA7NqyIuK91je3YlBHfrZkJgqn2\njLg3OeMppuxiGDd0xPQI3l/TAqvE/K+eMG0HnTkbNYlo0Uq22+YEM63aM30fihERERERERH1BwZT\nQ5SmlOlEivwtdmRNVMW3v41vwMjd9wBRJVVXFwRTI0YEFVMymKqrg6FH/GBKd5RgygujAPghVaVS\nKGZ0BBVEoWDKq5jSTVFxZkV0RB0bpq777yHb+mylXVAGP6YXCOa39OVsB89/tAk///fCbfwyuid3\n+autKB5M/eqZYLdAGZwV09bFGhEREREREdFAYzA1RNlKlZQGDa8v2xIaet2ZtXs0X2rAqBVTGzcC\nY8YElU81NSK4ikTEnw0bgLFjocNFVU4ERFGlIgrNzcGxFzxFlBlUeloJpjqVY9ME4nFEvBlUZkSH\nrrTynbpXA3K6CM/mr9jiPy2VE6FY1hQ/40a4XTLqVbM9MX/9Nn8d3ZHD7KsTRrfzq1pS4vMcsks9\nPjO9IbR255zlaMuYmL1ItESub0lj1tMf4/IH38eaplTBaxERERERERH1JwZTQ5Q6X+ijDW048463\ncNE/38XkK5/Ea0u3oD1rDY2KqVwO2LIFGD/e330PyWR4OPqGDcD48TCUsCniKG1ystIKCF5DafWL\nZ5TA5cQTgYcfDu4hFvMrpuyIDsOx/CqpGWMrYXrB1BNzV+Bbbz2C+++9EmnTguu6aPJme+Xvzpfw\n5nrJAAsQc6nUOVDbS4aOUT2CiFYYTCWjOm54ahHmr2nB5vYsRlXF8OC3P4U7zz4odN2G1gx++NB8\nnHf3PMxd0YTrn1yE215ehkfeXYe/vb6yx/dHRERERERE1BMMpoYotTpqhTfs+rmPRBXM1+98C62p\n3OAOpmTFlGyRq6oKQqVEIjwcPZUCqqoQyQZDzEMVU+pw81zhIHgZPAEQgVV9fXAPsRh0r23Q1HVE\nbRsn7j8JAKDZNiaMrgEAfLx6K8Z0bMXem5bh7ZXN+N597yFjir+Dcw7bOfR+iSID56f99Glc+cgH\nXX8nEC17GTMItOQQ9ZwSTKmtfLuPrcYhu9Qjbdq4fc5ynPqn19DUmUVdRQwAYOjhf+IPvbMWry/b\nCkDs5jdncaO/1lULIBEREREREVF/YDA1RGWt0oO19YiG+Wtb8erSLSWvKTtZEZX1horH4+FgSq2Y\nymaBeBxaNhhArrvK51fDKOUa/1ql0ioUeHmtfIbXymdFDIwwgD+cdbC//vUjpwEAYrYFSzcQtS3M\nevpj/OeDDQBEMPStI6aE3k9WULnecHRZKfXgvLXdfi37XfscTv3jawCAf89fj72vfhafbGz3B9zH\nDA1qJ98Je43F2JpE6DWeXbgJFV2Ekg1Vcf+4PWvh+D3HiM/fi4ouIiIiIiIiop5gMDVEmV3s+GYP\ndMBg28BNN4XnN+W77z5g8eLgsQyIZJC0ejXwn/+I42QyHCBls0BjI3DeeQAAS4sgFVXCGDWYKlIx\nFbGD6qpUZxpNphtcG4sh4jrebny6qMTSNPHeuRwqqpIAgJhtwowYMNSh6wCq4kZoED2gBFPe44wV\nPGdbqpI+2dSOjGnjkvveAwAs3dzh/30bkUjo/arjBr5+6E4Fr5GMBv+0/3ruQThpn3H+40/vNip0\n7dHTR2PamCqkc+HPRkRERERERNTfGEwNUbkuKqak1688ZgDuBGJm0w9+AFx9dfj8zTcDt90GuC5w\n5pnA9OnAVVeJNdMUu/F9+9vi8a9+Bdx9tziWrXyaBnzxi2IGVWsr8PTTAIDLTr4Cdx5yevA+akVU\nLieep5CtegDw91eW4ax73g+eFxMtb1EveNJliBWLAaYJPRH31i1YER2660BTqrUO2Lmu4OuQM6C8\ngim/5Q8ANrVlCq4HRFXV6q3BLKz1LeougCYWbRCD4aN5rXmW46KhOo58SaWd8Jjdx+BPXz8Ae08Q\nbYl/f3NV6NrpY6tQETPQyWCKiIiIiIiIBhiDqSGqq4opadyIRLfX9Am5K15bG3DrrcDee4tU5tJL\nge9+N1xJ9cgjYsbT4sXAK6/4YVPIwQcDDzwg5k89+qiYMZVM+sutiSokLKUyyrJEKyAg3mvMGH/J\n0TToch5VPI6obSGne5VYuZz/PNGqpwfVVbEYkMshkhDfYdSxYHrPiyqD1a84YXrB7efXq6WVmVHH\n3TTHb/FT3TZnGY789Yv+483tQUvilY8swM8eXyjeWw+Hbs2pXNGZVvNWNRecO3r66IJzADB1TDVG\nVcWxtpm78hEREREREdHAYjA1RG1LxVR+i1m/kRVL0Shw4YXAwoXAunXB+tKl4Wubm0O75oXU1Yld\n+Nrbg1Y+AKio8A/XjBiDipxXUVRfL16zqko8dl1RmeVJVdYEVVCVlTAc2w+YZCsfIFr1rIgBO+sF\nXrEYkM36FVMxy4Tl7dZneEHXwZPrEDO6/idk2U5Bi9wuVz2FzXmVUy990hh6/LXb3yz6erJi6p5v\nHgIA+MK+40LBVHVCfLb2jFXw3NFFKqsAoCYRxZSGylCVFhEREREREdFAYDA1BLmuiz+/vLzLaypj\nhVU0/aajQ/xUqpqwcWNwfNJJwbF6TTHqekT59aytDd4uXoHKnBfsjBgBtLQADQ3BtaOCGUqZyhpE\nHEeEZrEYDKXyCbkcXF18T8dVgE1VAAAgAElEQVTsWg8zosOQgVk8LiqmYuJaw7FhRYLjI6aOwi1n\nHND1ZwHQkjbR1Fk492pVU7g6adqYqm5fS3XktAasnHUS9p1YGwrHbv7aDABBQKXac3xNyderjBnI\nmA6sbajEIyIiIiIiIuorDKaGoHdXN6M9Kypi9Ei4Kuq2sw7AdaftjSe+d/jA3dDWreJnMhmESWow\npSshmdxprxR1fVwwsBtTp/qHZ773NC59/T44lZV+ZRMOPDC4Vgmx0lU1yEQTwNy5QDSKqG3DjHjv\nkcshDXFvu9TGYemGXw0lW/mMmKgyMhwLh+0+VtyibeHsT03G2BKtkmqrXksqh//68xsF16Tyqqjk\nrnv5Ljl2aujxPhNGFFyjhpDjaku3b+4+NgimRlXF8Juv7IenLjlCvEZcvEbK5JwpIiIiIiIiGjil\n95SnQWtTWzB/6LBdR+KVJVv8x5/de1yxp/SvlFf9Y1nB4PEtwT2Fwqbu2gvV9r2pU0VbIBC06gEY\nXSPCIq2+XlzvOKEwCiOC8KZj5GhUbFwH7L8/EI3CcJQZU9ksHlq8BecAGBEFzIiOqNx1T86YiotW\nP8NxcNx+E71jC8fvGcyxyqfuitiZDYKeqz63O258+mMAQFs6vDtfqdbMy4+fhsuPn4bOrAXLcRGJ\nFH5/mqbh8Ys+jfvfXo3R1aWDqcq4gSs/tzumj6nGzCkjkVQCrQqvMiyVtVGT6CY8JCIiIiIiIuoj\nrJgagp5csME/VkOERy88rBy3I2Y1ASKYkq1wLS3BuhpMFRn8HaJWV02YEBwrM6a+fPg0AIA2cmRQ\noaUGU/JY19Exagx0JWyK2uFWvnlrxG53MceGqUeRhBJMZbMwYuLeDcdSdvCzgP/3/4Bddin6ESwl\nmFLnNqkVVvlBVLabFrrKuIERydKB0X6TanHjF/dFVVx8tkvzKq2k7xy1K47efXQolBKvLx53ZAtn\nUxERERERERH1F1ZMDUFPfhAEU2or34yd6spxO8Hwc0sJNdLKIG11VlR3wZRaUVWnfJ7KSv8wUenN\noVIDL6VKyg+mbrwR7usLYcjh59EoohnLnxX15icb4RgiLDp5r9Fo33cC6te/F7y2aSoVU7b/flHH\nFmFcU1PRj6C25X33n+/6x7IqCQBytoPG9iwavIHkxSqmdh9bXfT1uxIzIlg566TuL8xTKSumcgym\niIiIiIiIaOCwYmqIk8HUr760b/luQlZMmUp7WkbZdU49H+nmVy6u7BxXrQQzSjAlK5egaUGQpQZT\nstJqxgxoUQO644i5T37FlKgOeuPjjchp4n7icDCqvhoR0/ssXjAlWwv3aqjw39ewLXHeDLfjSWrF\nlHToLvWorVCq295bh4OvfwHzVopwq1gwNb0HwVRPVXgVU2rrIREREREREVF/YzA1xDh5oUdEBjPd\njG7qV2orn6QGU7YSdnQ3Y0qtkioVUsnzajA1cmRwbqwYUg7T9OdKWY7r7cpnw9UicCIRGI4NM6IH\n9x6LBWGTDKa8KqkzDxzvB1MxxxLnreLVReOKDEXfc3wNxijzn+auEIHU315fCQDIWjYOnlwXqnaK\nGwP3z1O2AHaylY+IiIiIiIgGEIOpIaY9LzjQvb/B/MBqQG1PMNVdK9/eewOnnCKOE0rAo1ZEqRVT\nUk2NqMb62teCdctCJGog6tgwLRtuNIqYLYInVzdgODZsGUzJEEp+FsMQn0cOY1dCqge+eVAQXBX5\nPH859yD88MTpoXOO4/pD21X/+WADXNdFznIQ84Ko3UaLQe/H7VF6wHpfixvie8iWGMJORERERERE\n1B8YTA0xF98bzCyaWJfEMbuPBgDsNX5Eqaf0v2LBVEdHcKwGSN0FU5FIcI0aTBVr5YtEgt3/qqtF\nJdWECcHsKdMEouJaM2vBMaJicDkAWzdEJVVECZ5iMfEZXLegYspfB1ClOUFgZRe2vo2uTuCU/caH\nziViOhJRveBaAEjlbORsxw+Hnrn0CHxwzQk4Ya+xXX5VfUmGYrkin4eIiIiIiIiovzCYGmJeWSKC\nmDMP3QlPXXoEPrv3OHx47YnYZ+IgCKbkTwD461+D40MOCY6LBR9nnBEcRyJBW56uAz/6EXDiicUH\nnVdWit364nFg6tSgFU8Jk7SoCJBymSycqGjlA2QwZcOSM6+U4Ml/DWXGVGg9lwsHVkWoQ+kB4Isz\nJgIAXvnR0QXXNnXmRMWUV/5m6JHQbosDwQ+mWDFFREREREREA4i78g1RX9hnnB9eyPlAZSPDGRlM\nNTQAxx8P3HsvMGcO8OSTwbWOIyqoLr4YuOUW4MYbRfveffeJ9UgE+NnPxGt+4QvAWWeJ84sXB69x\n5JHAeecBF14oXn/LFhFmyVY8GRpZFiJxUXVlpjNwDN1v5bN0HYZjI6dHg3uP5h1blrgfwygMo5T3\nKEYNpvafVOsPMp9UX4EpDZVY3tjpr7dnrFArXznIUIzBFBEREREREQ0kBlNDVE1yYCtqupS/K98P\nfyjCpnvvDc9tAkQwdfLJwC9+IYKp/PVIBJgyBfjHP8LvoVZMJZPAnXeK4wMOCM7HYuK1lCont0IE\nU3ZHCrYR81v5LE2HYVvIRL25T+l0uCJK3XUvmSy+rn7mPBGlfbGpMxdaq4iFW/o6soMgmPLe+7aX\nl+OsmTtD625IPREREREREVEfYCvfEFX2KimVDGfa2sRPOThcHqvhjW13vZ7NFn8PdcZUKYmEGLqu\nVDZFEkkAgJVKwY7FEZcVUxEdUcdGVlZMZTLBTCv5GvK+5Ovmr6ufPY9aMbW6KRVa+8Vp+4Qed2Yt\n5OzyBlNyB8B1LWmsbU6X7T6IiIiIiIhox8JgaohYvTWFrBXMZ2qoLtzhrWxkOCMHkauhjnosr+1q\nvbW1+HvIuVNdqagAUqlwaJT0gqnOFNojUSRMEXzlIjoMx0LGUCqmKirEsXyN/Iqp/HX1s+fRu6g4\n2n9Sbehxe9ZCZ9ZGRYnh6ANBtvIBooKLiIiIiIiIaCAwmBoCMqaNI3/9Ir7/wPsAgB8cPw2Vg7Fi\nqrFR/FRDHTmQXNXV+pe/XPw9dC+0+c53St9HRUVBy13Ea+WzUmk8tawVSUsGUwaitoWsoVRMqcGT\nbAsEgoqp/HXvPbBiBfD666FbiSj/sq44YVrR25Vh0CX3vYe0aWNkVfnCxohS4dWaLh62ERERERER\nEfU1BlNDgGmLgdRPLdgIAEjGyldZU5RpAiedBLz0kngsh4UDQfA0eTLgukErX/46AKxbB5xySun3\ncV3g1ltLr8uKKaXlLlZdBQDItbYhHU2IiinXRWckiriVQ1rOmEqlguApnQ7CKPm6nZ2F69574De/\nKbhvtZVvdHWi4Fbn//wEvPr/wjv0jayKlf5sA0j+vhERERERERH1NwZTQ4Bpu6HHgy6YyuWA0aOD\nKiJ1oLmsPJJrspUvf10+rzeSSREwxb2wKZNBxWjRArh86Xqko3EYroOYbSGrRxG3TGSMONxoFGhp\nCYKnzk4RPMl5V7W1Yt1rC/TXvfdAXR3Q3CwGu3vU4edRo7Ctb0RFtKBCatQgCaa4Mx8REREREREN\nlEHUD0alWHkVLMkyziIqKj9sisdFeAMEFVEydMrlxHqxiqneBlNnnQV0dIg+ulgMyGRQO3EMAGDZ\n4jX+PKmkmUHWiCFu5wBNg1ZXBzQ1FQZTmYyo0qqrAxYvFpVesVhhMFVfL0Kp9nZgxAgA4YqpmF78\n70u9BgBGVg6OuWGsmCIiIiIiIqKBwmBqCMjlBQUT6yrKdCclyOBJVhipVVD5wVQ2W3wd6H0wdeaZ\nwXEuB6xZg+qxowEAu0ZyeMtr26swM8jpUYxv82ZiyYonufOfDJ5cV9ybDK4AcY0aTHV2inVAXCOD\nKaVialt32xssrXy5vAo9IiIiIiIiov7CVr4hoDNrhx5PH1NdpjspQbbqFWvPy2/lK1YxNWoUsO++\nwTV95d57gZoaAEA81Y5UTIRJFWYGn1nxDia3bMAbVxwhWvVaW8PB1HvvieNXXw3WgSCYktPNr7/e\nD6PUHQXVYeLbHEwNkoqpS+57Dy8vbiz3bRAREREREdEOgMHUEPDTxxaEHlcnBlmhWzYrwiZZMaUG\nT7KSKh4X7W6mWThX6pvfBObP733FlOqGG4DHHweiUaTiScQ625GOimDqbw9dg7cm7gUAGGc4hcHU\n+ecDn/+8OG5tFeuplLj3rVuBu+4Cxo8X6xMninV57Z13ApoGPPusfyvRSOGMqXy//cp+g2p22J2v\nLC/3LRAREREREdEOgMHUEPDu6pbQ48g2BB0DRoZN+VVQxYIp2bKXX1HVH666yt8pL5WoQryjHatr\nxwIAJrVuwuN7fUZc98ILoqrq7beDljwA2Gcf8fOuu4KKqOZmsSMfADzwgPj5z3/6VVlYtQr48ENx\n/NnPIpnL4Gez70C0s10EWrNnl7zdLx04sbefuNfe+elx/rHLbj4iIiIiIiIaAAymqHfUKil1xpSc\nJaVpxSuq1ON+loknEcumsKJugn/O1rxf/a98RbTrOQ5w//3Bk+SMqCeeCFKak04K1tX7luvnnANM\nCN7jO289jPPmPY4xD/4DuPBC4EtfEsPSB6mo0nLIAehEREREREQ0EBhMDQGH7Tqy3LdQmgyYEolw\nFZQMo+Q1xdblcX/fYjyBWDqNnBG0Co7paAou2LBB/Hz//eIvIKuk5s0DkklxbCjtlPIcAIwM/q4O\nSovXtcaMBY46SrT6tSjVby0tuGHdy6XLk1wX+NOfws/J99hjQZVWMXffDdx4Y+l1RUxnMEVERERE\nREQDi8HUEDCpPtiFb/exg2zwebG5UrIiSg2miq3L4/6+xUQFYpkUAOCP37kBn/3vW/DB2Klicddd\ngwtraoCLLhLHavAkWxAB4PnnxU/Z3gcEO/QBwNlni59f/zpGb90IANASCeDpp4PXisdF4HTVVTjz\nH7/GiqMiwAcfAHfcIa457jjgq18Vc7cuvlhUYrW0ANdcA1iWGLg+Wuw2iNNPF22HrgvMmgWsWycq\nwDRNtBbOng3cdts2fU/RUDDFXj4iIiIiIiLqfwymhoB0zsak+iTe+9nxeOyiT5f7dsK6G3gurym2\nLp/Xz3JKMIWvfBkfj94FL+16EJorRgAzZ4o2OwCorgaOPlocaxpw2mnATjsBxx8vzu20UxBkpdPA\n738vjtWqL8MApkwBANx+2sUAgIhtieAJABYvFt/DD37gB17amjXAfvsBF1wgrpk9G3jwQUD3hqF/\n+CFw5ZXAtdcCjzwC/PSnQGMjYCu7NX70kZir9bWvicHvgNhZcOedgbVrw9eWoCuzy1gxRURERERE\nRAOBwdQQkMpZqIgaqKuMIREdPDu3AQiHTaVa+XK54uvAgARTd55/Nb5ztmhn0yPBr3xbslpUIF13\nnTgRiYTDtLo6MXtq5kxg+nTxU65nMkBVVfAm558PjBsnjuNxIJPBMUeInf9GJfTg87a2ip+yckoe\nS/I7kvcg1zs6gveV1GPLEj+bm4PjZFJUdjmO2FVwOxy+26jQ44xp44J75mHFls4un5fO2XA5OZ2I\niIiIiIi2EYOpISCVs5GMDbJASlJ32lOP1WDKNEUwlb8ODEwrX209llmiqslQqoISyZgIcWRYZdtB\nC588lpVGul58XR5HIiIAAvznfW7GJABApaEFIZKcVwUEA9bVIKetLTiWz3Hd4B4dpZJJDZtkoOU4\nwbXZbDD/Kp0GVq4EFi0q9TWFVCeiocdvrWjCcx9twtX/XljyOR+tb8MeP38Gzy7ctE3vQURERERE\nRMRgaghI5WxUxgd5MFWsIkq2uMlgqkwVU1E9CKPUdrXR9VUiVJItc44THFuWCJhk9ZGuh9fVYMqy\ngnV5bf66DJlkmOS6QTClhk1NylB2NZgqdq06FH3ePPHTNIE5c8RxOh0EU1u3inlVX/kKcMYZwBVX\nhL8kxwHWrAEAnDvv3/jyZV8LLUcjGsa3bYZp2sDcuUBDg3hNxYufbAYAvLu6GQDQmbXQmjZBRERE\nREREVAqDqSEglbORjBrdX1gOpYIp0+w6mDr2WDGjSR0c3k8MpX3P0DXM/fGxeOenx0GTwVNXFVMy\nmIpESldMydeQ1VWy0uqmm8TjbDYI4tQqJxk2yTWgdMWUvEadFaVee/31wTlZlZXJBMHUjBnAk0+K\n17n/fuC3vw1/SbNmATvthJ2b1+Oa2bdjwsJ3Q8tVK5fi9Vu/ic8990/R+rhlC/Daa6Fr5Fwq2W56\n8i2v4pQ/vgoiIiIiIiKiUgZp2kGqVM5CxWBv5csPnnK5roOpgw4SfwaAkVcxNbrGC8Nk8FSsYio/\nmMqvmJIVVYCoIDJNUe108snB+t//Lo5lNRMAXHZZcOxVKKG5OTj3y18WrkciwBNPiOOHHgrW5UB1\nIAij1OAqnQ52D5TBlhqCqbzdBie0bi66HF+3FgCwz0dvAxNrxMm8UNFxREuiLEpb3s08KiIiIiIi\nIiIGU0PAqq0pfGrKyHLfRnHFgik5TyoaFdU+tl24PoB0LQim1BlTKFYxVaqVT1ZEFauuOvdcoLZW\nHP/nP4U38Je/FJ5zXeCxx8Tx3XcH5//1r+BY7hZoWUGllVol9ZOfBMcy3FKHp6fTwLp14ffND6Y+\n/ljs3Od9bt1VWgVNU+z+F4nA8r7DznQ2qOQyDKC9Hdi4EZg6FY0d4r0jyvdNRERERERE1BW28g1y\nSza1AwAef399me+kBDWYymZFcKPrhQPP5ToQVFINkKwVhC3qrnwFFVP5rXq6Hq6Ysm3RfqfrwP/8\nT9A+B4TnPW1v8LZhQ9fr6nB0NfTZY4/gWLbsqdc++6yYK6VSWwktS7zGccf538F5bz8erGcywOTJ\nwLhxMF3xvoevmg9XVmVls8C3vgVMmwa0teG+uavx1fnPonbVstBb2o5yT7/+dbgqjIiIiIiIiHZo\nDKYGuY83imDqoqN3LfOdlJAfPMlh5rKVr1hF1QAMPFelzaC9LVQxJYMndce77lr55LG8ppi99+76\nhvbfP/y4uzlbahh1+OHAiSeK48svBw47TBynUsC4cUBHR3DtsnBABCC8vmWL+Pn66/7n/MyKd4L1\nzk7/7y+2IngtZ+UqcdDY6FeIZW/6PU5a9Ap++cwt+Pr5JwHvvIND1nwIAEhvbQZuu02EZrNnAw8/\n3PXnJSIiIiIioh0Gg6lBLpUTgcFpMyaU+U5KyA+eZOjUVcXUAAdTasWOXqyVT9PEn/yB5oYhwhTH\nKRxuDoQ/hwyLAKC+Pvwzf33ffcM32NDQ9WMZjgHiPmbNEsfZLHDmmcGaWommacD6bqrs5AwsIDyv\nSvrVr/zDPa/9UXBefg9XXeW39cWvvRpf/eA5AKIdMHf5FXjw3itRk+mA8Z1vA9/9LvD228CYMcCm\nTeL5L7zgz7YiIiIiIiKiHVO3wZSmaX/VNG2zpmkfKuf21zTtTU3T3tc0bZ6maYd45zVN027WNG2p\npmkfaJp2gPKcczRNW+L9OUc5f6CmaQu859ysaRxQI63Y0ok1TWKotdzpbNBRB5qrFVPFBp6XKZhS\nf6GiyiB0XHNNEL7kDzeXc7EA8Vl0HXjxReCMM4Jr1M9RXa28ifc8tRKqoiI4Vp8nq8rU9ZHKPDH1\nO6ysFN+hbNtLp8PvobYQVlWF2/aK+ZESNskAEUBLokoc/O53RZ/myn+i69eLVj6PrbRJfrBBVPpd\nMefviCz6yLvABkaNEkPiAeAXvxB/iIiIiIiIaIe1LRVTfwPw2bxzvwJwreu6+wP4ufcYAD4HYKr3\n5wIAtwKApmn1AK4GcCiAQwBcrWlanfecW71r5fPy32uHdfRvXsIfX1wKAEgO1mBKhh8yNMlv5ctf\nBwY8mFKFZkwddhhwxBHiWFZElQqm5PM2bw6uUUMhtYpKrieTQRue+pnV4xEjxOvX1ATnqqqC49ra\nYA5WMlm426F6D2rFVHftgfn04PfLinTzu+YoLYzjxvmHRy8P2gBTpmh7PGrFO8Gw9LY28RnkDoLV\n1WJ4OhEREREREe2wug2mXNedA6Ap/zQA+X/SIwDInqFTAdzjCm8CqNU0bRyAEwE877puk+u6zQCe\nB/BZb63Gdd03XNd1AdwD4LRef6phwFWHWGMQV0zJ4KmionjFVGdneB0Y+IopdaxUqd94WTGltvLl\nV0wB4py8Rg1/5GfS9fC6DLTU0MhQNsPcbTdxg2owpVZPjRgh7iWREO+hBlNq66R6D/nvt50Mp8Ts\nLI+mztYaMaLLaxNWLqjGam0Vn8O2xTkGU0RERERERDu8ns6YugzArzVNWwPgNwCu8s5PALBGuW6t\nd66r82uLnC9K07QLvNbBeY2NjT289cEvZzm47IH3Q+dCs5EGk/zgSYYjbW0ibMkProAyVEwF310y\nahS/ZFsrpgyjeMWUvFbXw61+cmC6+pnVlrvZs4EJE8LBVGVlcFxdLe4lkRBhUy4XPP/b3wZefjm4\ntlRbX3cOOww49NDgqbbVxcWA6wS7HMJxgBkzSl5blU2heebh4oGsmAJEFVVVFYMpIiIiIiKiHVxP\ng6nvAvi+67qTAHwfwF+888XSE7cH54tyXfd213UPcl33oIb8AdHDyIfrW/H4+90Mrh4s1OCpvV0E\nKY4jQoja2nBwNWUKcOqp299m1of2GFddfEHXw8PPi82YAsIVU2plk6xQ0rTwuqx866rlLpsNB1My\nvAFERZLjiGArP5gCgD/+sfjztqdi6qmnggANXpWTorVhHGzln6rR0hIsdnQA118PAFhfPargpSvN\nDD684lrvhVqDgK61VQRT8vdj5UpgyZJtv2ciIiIiIiIaFnoaTJ0D4BHv+CGIuVGAqHiapFw3EaLN\nr6vzE4uc36E5TslsbvDp7BRBSSwmhlrX14tQynXDwVRVFXD66cBjj/WqzawnZCvfbWcdiNqKEu8d\niYRb+WQrojxWK6bkC6qzoOS1kUjx4EoNk/J3z8tkwsPT1efV1orvUrbyPfBAeCe7yy8PjmVbnWEE\nIdXZZxf/vNK0aeJ5H/p7G0B3ndAli476PPT8vHiffcTPdBrZSvE9PL7HUf7yW5P2AgA0VtZiqxYT\nn7O1FfjIG4R+333BvCnXBc46C/jOd7q+VyIiIiIiIhp2ehpMrQcg/y/0GACy1OHfAM72duebCaDV\ndd0NAJ4FcIKmaXXe0PMTADzrrbVrmjbT243vbACP9/TDDGWb2jKYfOWTeG3pFnRkw61UR04bxNVh\nskoKAJqbRZAih1vLKiogHLwMsKDWp4vAT1ZMyYqebLZ0xZTl/f2oLXdqMCVfQ11Xwyi1sgkQr6de\nmx9MAcBrrwGyUunaa4N1tdJKDiJX52PJ5+f76leD5zuO2GFPce9+wR4Eb1hVKDB3rviZTuOuj8R9\nJaysv/zYnkdj4egpWFfTgEff3yDuo7kZOPfc4F4rKoJ5U/X1wW59REREREREtMPoNpjSNO0+AG8A\nmK5p2lpN084D8C0Av9U0bT6AGyB21QOApwAsB7AUwB0ALgQA13WbAFwH4G3vz/945wDRFnin95xl\nAJ7um482tLy6ZAsA4IG316AzGwyXjukRXHLMbuW6re51dASVQ5mMCBvkLmyJhFgHwtVFg1EsJgIS\nGSDlB1PyvGEEwZQatslWPdcNgik1YFJb+YoFU+q16nGdt3llYyOwerU4llVHQHj4uPo8GaSpwZQ6\ndH306OD57e3ivicGxYvrakQYmovo+BgiNLvi85cCANJGXHye0aOBdBp3LGzznjPaf37cNrG6diwS\nZg5vLN8Kd+RIETyNHSsuyOWC7yGdZjBFRERERES0g9qWXfnOcF13nOu6Udd1J7qu+xfXdV91XfdA\n13X3c133UNd13/GudV3Xvch13V1d193Hdd15yuv81XXd3bw/dynn57muu7f3nIvd/O3odhCNHaLa\npKE6jo6s6Z+f+5NjcdDk+nLdVvc6OoJqH7ljXLFgqpwVU17JVJe/WXJ+U7GKKXUnvGg0mMekVjnJ\n56nBlBpAqcPP1fO//jWwcaN4ffl+6rqsiLrwwuL3LYMrIAijgODvQA2ubr89OJb3PmIEsHmzOP7F\nL/xlUxchVtaIoy0hrl07YgxuP/h0OPILragAUil/PW6bWHLr3QDEnKp0NI6kV0XljvAqpmRAl04H\nQVo6HVRUERERERER0Q6lp6181AdM28ENTy3Cyi2d+PsbqwAANYko2jNBK191Yjt2VyuHTCYIG7LZ\ncDCVTIod277//bJWTJ2833gAwB7jakpfJIOpYhVTlhWumJLzpopVQclB5UB4rlSpiqnFi4Nr5Xm1\n8klWOe2+e3Bu2rTgWA2eNGUvAdlOqa5PmVJ4P1VVwMKF4niPPfzlj0aLa3O6gc6YuK/KXBpZI4a4\nHI5eWQl0dOC4/SYhqxuoyqX91/3M5Gocc+AuqMiJ3wVX7sAn3zeTCcKxjg4RXHZ0dJMeEhERERER\n0XBjdH8J9ZeXPmnE7XOW4/Y5y/1zv3thcegaPVJs48JBRG1zK1YxddRR4k8ZfWHf8fj83uMQ6eq7\nlMGUponjUq186q58+bvrASJYkefVCia1YqrYUHTDEK/R1gaMGROsm1713JQpwYD2++8HDjhAnJct\neQ0NQTB1xRXAHXeIYzl3CigeYsXjwOPeWLdp04AzzwTuvRevTd4PLgAroivBVAZZIwrDdeCaJrTa\nWqClBabtIhVNosJMI1Ihrv3RUZOB9MfIZkXY5FRVQV+3NhxMyTbDlhYRTLmu2OXxgQfEd3fOOSAi\nIiIiIqLhjRVTZbSpLdPl+h/PnDFAd9ILcvc61y0MptQwpsy6DKUAca+5XHDc1YwpGTipn08GLmrF\nlOMUrgPhiik1mJLPmzAhWP/iF8WfCy4IXk+dGzVjBvClLwFPPhms19QEgdVRRwE/+AFwzz1BIFZZ\nGbzvpz8twqvzzvMH168ctwtcLYLWeCVemnIgOpSKqYwhvgero1O0ETY3I2c76IwlUZVLQ5OfLZUC\n6uoQty0krCycqmpRMUVEmhsAACAASURBVBWNioDNWwcgWvhkq2d7O3D33cDf/gYiIiIiIiIa/lgx\nVUan7j8eP33sw6Jr/3XQRHxh3/EDfEc9IIMpORA8Hg+qfKKDvA1RJSum1OPugqliO+2VCqbUMKpY\nxVQ0GjyvqkrsmvfAA8A++wAPPyzO/+53wE9+IoaUv/GGuI9kEvjXv8S6bQO//CVw6qnA2WcD8+eL\nEOo3vxHr7e3i+ptuEusNDcA3viHWZAXVzJl4t02EaIsbdkZTRS2yXhgVt3JIR8Wa2daOaE0N0N6O\nrGl7LX4mNBkwdXb6YVNVLg0nHheBpaaJz9fZGczPKtbi19hY/O+JiIiIiIiIhhUGU2Wkzo+KGRHk\nLBFkJKIRXPiZQbwTn8o0RdihhjpDPZiSO++VCqZkiFOslc9xglY/dV6SWjGlPk+GXIYhAqNTThHH\nco5URClqvOwy4NJLxfvPnFn4GWbOFO8v72/nncPr1dUiEJLr559f+Bo/+hFGntYI/HUu/uvrvwIA\nVGVT4hYdC0d/anfgOcBq88KkbBY524EZ0WE4FiLV3iyxzk7/M8dsE64RDX4vZDBVbNC8aYr1lSsL\n742IiIiIiIiGHbbyldlz3z8ST196BN686lj/3MfXfQ6TR1V28axBRFZMyVAnGh36wZSsAFPDEhmi\nRCJBsGMoua4c5G3bwfPUYEqtklKDKfW11JDKtsOvn399Kb1dB3DUtIbQY8sLxwzHgVslPud1988V\n30kmg6zpwI7o0B0bkSploLn3ncUtE46uB1V13tD0UJWU/KyWFaz3M9d1cdUjC/DGsq39/l5ERERE\nRERUHCumymzamGr/+JJjdkNnzi7j3fSAbHlTw6ihGEz94Q9BkGQYQeAGiEHiskLJcYIqJnW4uWzV\nUwMl1xVBkOuGK6bUwEkGRZoWBDeyYkt9/TKyIuJ+dceGlhQB2/JVjXAb4tAyGVExpeswHBt6lVIx\n5Q1ej1s5OHEj+HwVFeGKqUwmaOuzrGC9n2UtB/fNXY375q7Gylkn9fv7ERERERERUSEGU4PI5SdM\nL/ctbD8Z4Az1YGrPPYNj+Rnk/T/xBHDEEeLYsopXTMlgynWDQMl1g7BLDabU70WtqlKDKdseRMGU\nuI+oY0GPeSGV68CKxhDNZpG1bNiaDsO2oce8z2bbfvAUs01sTtuozOWgA8Hnk99JNht8l6YZrPez\nnO10fxERERERERH1K7byUe90FUwVa0UbCvJnTMlzgAhMZDClBkdq8CTX1dBJHX6ufi8ygDGMwoqp\nwfL9aRosLYIpdQlEvOBJd2zkomKemJmzYOkGDFcJpizL/07iVg7PfrIFdi7v86kVU1Hleep3MWcO\n8Npr/fKx5Ew3IiIiIiIiKp9B8n++NGQVC6YymeB4KMqvmAKCEKpUxZQMWYDiwVSpiikZTKkzmAZZ\nxRQAGLEoTtmzAXOjMphyYOri2M1mYUUiiFkWomqA51dMWbAiBmKOJb4T+fnU4eelKqauvFLMnHr+\n+T7/TAymiIiIiIiIyo8VU9Q7uZwY5q2GKkOxlU8lK3YqK8PngHAwtT0VU6VmTMnXsO1grlJXw8/L\nxftOZCuf4dhIu+I/H+2dWVgRQ8yYMiJByGYEs6nkAHU4TrAuB8mrA+PlbC35+6Tr/dbWx2CKiIiI\niIio/BhMUe+YpgimhvqMKZX8DCNGBOd6EkzJEAYIt/Kp34usGsrlgAsuCN5rEA0/n1Sf9L8Tw9tR\nMOI6WLChHYAIqayIDsOxENFQUPGlu2LXPgCFFVF5IVbR9f4KppQZU4s3tffLexAREREREVHXGExR\n7wyXXflUMhjyQhgAYqc4AEilirfyyV3lgHDFlAysis2rAsLBlDweZK18EU0LKqbi4nMYjo3XV7aI\nddfB6PoqjK+KoiJmFARruuPAjCjBnjpDSgZP+TOmXFcEe/04CF2tmHpj2dZ+eQ8iIiIiIiLqGoMp\n6p1iFVPnngt8+CFQVVXWW+sxNVz76U/FfCP5WTo6gPHjxXFlJfCPfwC33FJ8hlRtLfDyy8DFF4eD\nK7mu68AllwBnnAFcdlnwHpHIoGrl0wBxz5aFaDRoz7O99jzdcVBREcfImFJJpgZTrh2umCrWqqdW\nTKnzvNRr+1hWCaZqkoPjuyYiIiIiItrR8P/GqHdkxZQMD6JRYORI8WeoOuGE4PNcd534OXeu+Dlu\nHPC97wFPPQVMnQpMmxZ+rmEAp5wCXHWVGNxdUwMceGD4mupq4PrrgS99SbQL3nuvOH/AAcGQ75/8\nRARag0BE04ArrgCmToURF1VkantexHWg6UZ4h0ElbIo4jh9i+efzr1VnbakD1AdoxpQ6DoyIiIiI\niIgGDoMp6jnbFu1WasXUIKny6ZWrrio8d8ghwCOPiNCqshLYa6/Ca155BZg4UQRPN9xQuP7JJ0CL\naH/Dj39cuP7QQ8CcOcDYseLPIKFpAC6/HAAQffM9AF7FlCbCJsOxoUXz2vOUiqnjpo7Eex+0AQBc\n04SWXzGlzphSZ2vJ8wMwY6oz1z/vQURERERERF0bBikClc1wmiu1LU4/vev1ww/vej2/uirfiBHA\nySdv3z0NgIicmQVAM8Tfr+46cLxgSncdaIZesgoqrrl+iGWZFqLFhpt3VTHVT618N89e4h+nsv3z\nHkRERERERNQ1zpiinsvlxM9oNHxMw4oaTOkxL5hSZkxFHAeR/PY8pfIppgRTtlli+HmxYEq+Rj9V\nTL2zqtk/ZsUUERERERFReTCYop6TVVJqK5+6kx0NC0ouhfEjxYB2w3FgeTOmDMdGPB4NV0Ep7Xkx\nOHAiSjClVkHlhVihkKqfW/lUrJgiIiIiIiIqD7byUc8VC6ZYMTXsqBVTMmzSHdtv5Yu4DiqSsZIV\nU1EEFVNOTyqm+riVb0NrGs9/tCl0jhVTRERERERE5cFginpObd9jMDVsqbmUDJB01/HDJt11kKxI\nlAybzJzp7+Bn5cxwe548Ljb8vJ925Tv/7nlYuL7Nf9xQHUcqx4opIiIiIiKicmArH/Xcjjb8fAfy\nwAUzMeuL+wAoXjFlODZuPfdQAMDndm+AETUKh5971+4xujKomMpvz8sblF50+HkfB1MtKTP0eFRV\nHJ1ZVkwRERERERGVw/9n777DJanq/PG/z6mq7r5pcmJIQ04CShAUcAXWBURBXcw+i4uKuu666ndX\nHVz8CQqLOawuhhXTLpgBXVDXgCgZESQNmWEYZoZ7J93c3ZV+f5w6dU5VV/UNM3Nvz/B+PQ/PralT\n3V3dPjbc93w+n8NgiqaPrXy7rOP2XYiDlvUBAKRdMZWERu8+aUUaJr3v5H1bq6Csyqel3S7e9OIV\nAEqGn+erpOzqKfva7cTJvCGgt+qwYoqIiIiIiGiWMJii6WMr3y4titVPaQc5SYC0qOaUt9wVVEGJ\nJGz63QPrs3OjimZM2cPPt2PFVCMIsX5wHK71fj59zhGoeQ5ueXzTdnkNIiIiIiIimhoGUzR9bOXb\npcWxSqaKWvkK50KV7MqHIIB01PH/3PKkmjdVMii9pZVvO1ZMnf/du/Cif/9dJmjrrbpY3FvdLs9P\nREREREREU8dgiqbPbuU79FDg7/8e6Oqa3Xui7SatmCoYfp4JnvIBUm7GFMIQwjOzqQLI8mvtkCo/\nj2ob3fjIAADAfjs9VRd7LexW71e/YSIiIiIiIpoxDKZo+uxWvpe9DLjiCgZTu5AoqZgSdsWUlGqb\nvlxFFBwHiCIgjosrplwVNsk4wjdvXzMrFVOa/XbGmiGc5EQYM5giIiIiIiKaaQymaPp0MFWpzO59\n0A4Rpa18uYV2VU5RZNalTNeD5NiJI4TCyVZM5YOpfMWUvjaOt0v1lJ0/nbD/QjhOEkyxYoqIiIiI\niGjGMZii6dPBVJUzenZFOsARyCVTBRVRLQPLg0CVJiXB0mjS9elEIUIpTXVVPpjKP68dTB1/PHDm\nmdv1PfbVPFMxxWCKiIiIiIhoxjGYoulrNNRPVkztkuJ0V77cQlHFlB56n58LlQRLTamCJzcKEchc\n215ZMKXXw9CEWHqu2TbQrXxVN6nikmzlIyIiIiIimi0Mpmj6dDDFiqldkpe0uM3vzgWPRRVTOpjy\nfbMOpMFTPfmqcaMQkTAtfmkwJYTZ3bEspKpUtkswpdU89TppMBUymCIiIiIiIpppDKZo+jhjapf2\nwn0W4KOvOBSXvPrw7IIOk8qCqYKKqUP2WggA8MKgsGLqnMtvQV045vH2uv0a2yGY0h17umLKZcUU\nERERERHRrGEwRdPHiqldmhAC5524D+Z2edmFdq18JRVTOpjqdWKEBRVTf3pqC5rCQdxsls+b2oZg\nqhlE6XGcBFBH7TUfACCTYGqsse2D1XeEj157P75321OzfRtEREREREQ7BIMpmj5WTD03HXccsM8+\n5cGUHSDp6qpk/bVHLEXguOZaa7i5Lx2MjNRbQy77NaYZTI03TejUV1PP/7nXHwkA6fDzl3z6Bqwf\nHJ/W8+9I3731KVx4zf2zfRtEREREREQ7hDvbN0A7sd5e4IADgFpttu+EZtK116qfa9aon1MIpjLD\nz3PBVOC4GNw8jL58W6D9GtMMpsb8ID1uBhEOXtaH7op6bj1jCgAeXDeE3eZ2Tes1iIiIiIiIaOpY\nMUXTd+65wCOPAHPmzPad0GxwrconO0yqVEw1nd5JL1l3wgBNXTHVbGZ22vOlgz8/PmAq8PQ6sF0r\nppphBNcxYZQdTG0Z237D1YmIiIiIiGhiDKaIaHryVVL2sQ6Q9LEOpoIAgXQLrw2kCzcqqL4qet4p\nGrODqSCCI81Xnx1MhVGEThJGHMhORERERES7NgZTRDQ9euh9s1leMVWtZtadMIBvV0zpdQCBdOCG\nYbZiKv8a0wym6r4JphpBmO7EB2SDqU7bmO/JjaOzfQtEREREREQ7FIMpIpoeO0DKB1M6QKpU1O6N\nRcGUvjbZ3dF3XHhRYJ5XrwPmObZDxVQjiDLBlH3caQVK67Z23jB2IiIiIiKi7YnBFBFNjw6jrOAp\nbbmzd2y0gisZ+PClVTGVrNc8iUA6WN7jmufS6/p4W2ZMWRVTQ+N+ZsaUFFbFFDormRquBxNfRERE\nREREtBNjMEVE0+M46p+yiqk4bt/K98tfAiMjQLMJT0oE0kUVkQmjnn0WqNfVcT6YGh4GBgaA1avb\n3+MjjwDIDj+PYpTOmOq0Vr7hOoexExERERHRrs2d7Rsgop2YDp7ylU1xrHbSq1RUiOQ4gBDZ4ecf\n/Shw/PFAECBIdus74t7bgfe/X61fdpl5nUZDPdfoKPCqVwFr1gB3351dA4AoAs47D/jHfwSeeUZd\n+5OfYHyPY9Kn+tLPPgVvxV7Aed8DkA+mOiuZGmmwYoqIiIiIiHZtrJgiounLterB97MDy/W6EIDn\nQQY+Go5nHp9c++7f/w8AwIlC4BvfaH2dz30O2LJFHV97LbB1q1nzfeDnPweuvx7YsAH4zneAV74S\nuOsutX7ffag9cB/edM8vAAD7bn4Gy9evTh+eCaam/UHsGENs5SMiIiIiol0cK6aIaPpyw83h+0Ct\npo4bDRU8JcPN4XmQYYCGa4KphltBFcB7b74KT87fzTxvrWba+ADgmmuA+fPNnx3HHAcBcNZZ6nj9\nevUzDNX55HVfcvH78erVj+Lnh7wEdbeCvtC0yNnBVBjFeHDdEA5dPmc6n8Z2Z7fyPTEwghULeyCt\n+yUiIiIiItrZsWKKiKbvG98Azj+/uGKqXs8GTJ4HJwjQtCqmblgznB4Lu1xJt+bZdMUU0BpM5Y/D\n0Myjcl0sXP2oOkyCsarfNE9lDT+/6OcP4uVf+iPuWzvY/n3PkBGrYuqUz96I6+5bP4t3Q0RERERE\ntP0xmCKi6TvrLOCoo7IzpnQw1WgAXV3A+Lj6c6UC6ftoWhVT4141PQ6tgeSFwZQVIMG1ij3tnfr0\ncS6Y0t50zO6ou1XE+p6QrZjSNo40Ct7szMvvyvf0lrFZuhMiIiIiIqIdg8EUEW27ola+ej0bTOlW\nPseETuNeLT0OxRSCKTvEsiummkkllN3KZwVT6zYOo+F46ImKW/m0qtcZX43DjeyufH1Vdl8TERER\nEdGupTN++yKinZtdJVVWMVWtwmk2MhVTo1YwFUirPc+zBqRrdjBV1spnV0xZM6a0l+y3AA23ggUy\nNE9VEExJ0RlznPIVU82w08azExERERERbRsGU0S07fr61M+hIaC7Wx2Pjqpgql4Hogjo64MzOoJx\n17TvjVa60uOma1VJVc01qbJWv3e8wxzriinHKayYetXhy/DKo/eEG0fpuagg62kGUevJHejae57B\nkxtHW86P5IKpuh+2XAMAWLMGOPNMYHi4eD0IgNe+Frj77vKb+OhHge99r3z9hz8EPvzh8nUiIiIi\nIqJpYDBFRNuuu1uFQYODwJxkR7vhYWDuXHU8MgLMm4e+225CX30kfdhQtTc9rtvBVE9P62vYVVI1\nU2mVGYquK6YcJzt7KiHCEK7nqYoq/ZCwNYSa6WDqn79/D077wh9azg/VAyzoMZ9LaTD1b/8GXH89\n8NOfFq8/9BDw4x8Db3lL+U18/OPA3/1d+frrXw988pPl60RERERERNPAYIqItp0QKoSyg6mhIWDB\nAnW8aRMwdy6ckRH8w+0/Th+2pXtOelyfSsVUl6m0Siu0ANU+CGQrpuyAKgzV80QmePILQqhmQVi1\no8SxKtnKh2EDww1sHGlg4WSCKW2iFsQOaVEkIiIiIiLSGEwR0fbx2c8Cb3iDCaYGB00w9a53AfPn\nAwC2dJkwamP3vPR4zJo3lamO0srCqDFrp7pnn1U/5841wZQ9gyoM1XPriqkrr8SCP/y25aXGm8l6\nHKt/AGDtWmCffYAnnlBh12hr6x1e9CLgRz9SjxkcbF1/73uBD31IHSfrodVLeMkbViI6+RQAwF/+\n8GfsNjSAEw9YlK7XfSu88n3g/vuzFWN5Y2OFlWOpZtPMACsSRSpgJCIiIiIi2kEYTBHR9vHWtwIn\nnWTCqM2bgYUL1fHAQDoXarBm2veenrskPR63K6aKwpAjjjDH9oypzZvN8etep37Onw888og69n18\n97hXq2MdTOmKqUsvxUHX/6jlpQbH/fSxcBzgU58CvvtdYPVq4L/+C3jlK4He5H2ceCLwxjeqkOe2\n21TL29e+BsybBzz+uJrdtGyZuvY//kM91+rVav0rX0H4m99i9SdfgSXDm/CRH1wG+fsbAAAnvekM\nvPu2H+PIPUx4N25XTK1bBxx+OHD11a2fldbTA7z0pSZcyzv22GzIl/eJT6iQb9Om8muIiIiIiIi2\nAYMpItq+urtVK96mTcAhh6hzF1yQLsdWO9nqhXtgbLc9AADNmhWQ6MonADjvPPXTrqKq183xuee2\n3sOiRWrYN4A4inDT7oep80GgWvl0xZSUEGFre9zWsWSI+vi4CnU8z7xmrQb86lfq2PeBm28Gvv99\nU0FVqwG/+Y06/tOf1Owm+/3Y7++KKyC//B8AgBesezhzyeiS5Vg+1I+uinnfG0ca2DTSMO8FUMPd\n7eBpaAi4917z51tuyb52HJtz9nW2u+9W7+dHSWi3bl12/YkngPXrix9LREREREQ0BQymiGj7EgI4\n7DAV0CxYoIKQc84BLrgAo391Mq459KW4+JR34IqjzwIAXHvNzQiFxEj3HIzUkqHnK1ea5/vmN1VV\nUhgC73+/OnfooWb9ZS9Tu8V5HvDf/63OuS7wvvcBAHw/RCCSr7p8xZR9DGC/xer1x3Qrn25zq9Wy\nwZRmt9HZwZQe+l7WBqcrvoaGECXzrOzADnGMsXkLsHBsCF2eCaZ+//AAjv5EEnrpYMrzzOOEAE47\nDTjyyOzr6eBKCODyy4ETTgD+93+L723zZuCoo4C3v93M+tKzu7T99gOWLy9+PBERERER0RQwmCKi\n7e+uu4CLL86eW7ECG370Mwx29eGKY8/GxX99PgDgt6v6EQqJqgN8+O8+rq494gjgYx9Tx3GsgqYg\nUG1wgJo39etfq+MoUuthCLz5zcALX2iGnANoNn2EMgl39HmrYsoOpn77/16Ked2eGX6ug6muruJg\nynpsGkx1dZnnd12zblc1NZvpepw8R2gPd49jBEJCIELNK5i3BZRXTN12m3mvmn18++3q58BA8fPq\nSqh77ikPpoiIiIiIiLYTBlNENGOcgl3hfrPqWURSoiJi+HGyHkVmF74oMgPL7XP59ShSAY0Om3Qw\n5QcIdcVUEGSHn+eCKQDwHAk/F0yNOR6u+9NT6pw938p+rA6xPM+ERnb7oT2EXB87DuLkXiJhfR2H\nIdYPNyHjGI5s/cze9I3b0L95WP0hH0wVvZ6+HyHMUHZd1ZW/1l7XwZTdOmm/5yBQbZOf/nTr6xMR\nEREREU0CgykimjGyIJgCgEA6qCBGGo/kQ6h8MFW0rs/nrm02Q1ONlG/lKwimKo5EM0iCniSQeWir\nj8GxpGrIDpui3C55er2oYsquOrIqptJWPvsmwhChkJBxjIOX9WHpnCpqnvm6vuXxTXjbf93W+hr2\n51sWTNltjEX3Zr8PfY29s6E+njdPrW/eDAwPg4iIiIiIaDoYTBHRjJEl3zj/deyr8NAhx6CJCSqi\nAHNcFExZ1377VlXh5PuBqUbSj2tTMVVxpWnlSwKbwK1A6qok+00UBVP289tBUUkwFSfP60Wm3W5w\nuI56GGNhl4ueqovbL/hrnHrw0sx9uvr6/Iyp/P0A2WBJX2NXWdn3pq/V1Wf55xICOOUUNUdMCFVB\npt8PERERERHRFDGYIqIZU9SWBgBfOPHNWHX0SxDonKeobc+ucmrX1pesf+x/VwHQwZTVIlhQMdVT\ncbCoV7XojdZ9XH/301i7eTQNaeqQkLH1GK2oPU9KE+7Ys53GxsyxDnIcB3ESmlUC81wnXPprBBCo\n6Jd65ztx4k0/z3xmTlxQlWUrCpsAE0zZ96bbEO1ro8hcawdPQaDCMP1+PY/BFBERERERTRuDKSKa\nMWWtfAAwt8tDI7ZCk3zbnl0xNVFbXxgCQiCCgN8sqJjKzaO65//7G9y68lQAwJbBUTz+6bPx8HtX\npiGNn7TVAchWJdmBTFEwVTSDyr7WcaCvcCMTHsk4QgQBVzf4/fSn2Gf1qszn5dntgvre7Cooey5U\nUTBlnysKpuxr88GUHkYPsGKKiIiIiIi2CYMpIpox7YKpOV0emshVNunjfDBVtG4/ztrp7oG1WxDn\nK6Zyz+s5Ep6jnsPVw8hdM8T8P296CkJXTBW179nH9owp+9qyGVORCpOkFSo5UYhISDhWlZbMtRw6\nRa189jUTBVP2vdvVXPre7Va+dhVTlUr2uYiIiIiIiKaAwRQRzZiyVj4A6K26CFBQMTXNVj4AiITA\nhq1j2YqpfDBlt7QB8JLKpciqCgqlY4Ij+/qyVr6iYKqouspxECcBUyaYiiNE0qrSchysHsgOGE9n\nTLku0N2tjlesMBcUBVNxXBxMFVVM2dfm7z0fTLFiioiIiIiIponBFBHNGKdNxdSVt69BKEra89q1\n8hVVTCXBUJy04GVmTJW1CCZ04GMHU4F00uqlyLeqj8pa+SaqmMpcq66RsQm8ZBQhEgISZuB6vmIq\nE0zNnQv09AAveYm5oChsSlocM/dQdm1ZiNVoMJgiIiIiIqLthsEUEc0YUfKN84Pzj8dIo2AWlH3c\nble+kla+SAiIOC6vmLIHoSfcUAUzseNZFVOmeml4vKAlD8iGTfo57eqqkoqpLaPq+c4/YUW67MQR\nImENXHccOLDmRwGoBcl9dHWZ9jrb4KA5tmde6fdvV1QVVVcFgbl/+96bTaBaNY9hMEVERERERNuA\nwRQRzZiyiqmFvVX8v5cdaCqbtlsrnwp3nr/3/NZrSyqmPF0x5VkVU8JJZ0zd+shAeu26gSHzQB02\nCTGliqn+QVWttKDmpMsyjhEnoZq+7sX7zM/cZ28jmQvV02MGktsGzH2mwVEYqusBYMi6d3vGlA6m\nRkaAp55Sx3Zw1WioCi39+Eolu05ERERERDQFDKaIaMaUzZha3FfFyw5bilCWDDSfbsUUBGQc46SD\nlmbXi543oXfHi10vM2NKt/Ld8ZgJfFb+4M/p8dr+QXN/U5gxtducGgBgYbepeFK78knIKFRtdmNj\nWBrV8fAnTk+vuexXX1YHPT3AqlXAli3ZD/WrXzXHTz+tftqB3iOPmHW7ukrf+8gI8OCD6tgO1RoN\nYN48YHhYfT61WnadiIiIiIhoChhMEdGMKRsxNafmouLI7O55SYAyXm/iwQ0jiO2KqXbBlBU2xUJA\nxhFeevDSCa/V9K58YWb4uWnls2dB6RALAB5es0kd2O2BZcGUrkrKXfvk5y9Xp6NQtSFGMbBxI9Df\nD2zYgIrT+pUdd3cDN9xgAiVdOXXrreailSvN/ejXXrvWrH/+8633ZldR2RVRzSYwZ446Hh5WrYSs\nmCIiIiIiomliMEVEM6aslU8IgYorC4eUf/umJ7CqfwQj40mwM4VgKhISIo5RrXoTXqvpXfm6umvZ\nXfmSFj9dOWVfCwCVOFfRBWRnTJUNP7d2+3MrXvoaoZSqfVCHPkJAjIzgol9fjhf3PwK/UgUA/Opx\nq9oJAJYtA04+GXjnO9HCDqYqFXNeV0YBJkCLY+CSS9RxvpVPz7PyfVUxVa+r8+edB/zsZ62vS0RE\nREREVILBFBHNmHwr30/e/SLcfsGpAKCCKbSGTY1mgFhMd8aUauUrvLZkxtRnXn0YACC2WvIC4SBK\n2gx/etip6bWu9dh7n+hP3uQUWvmkBGJTMbWgr0udjiKcdOASiCgbTGHVKpz75+tw+XFzsObQo3HP\n7gdjqG7tEhhFqhJqv/2A3XZDi0YD+PnP1XGQe5x2883mfVxwAbB4cTaYGh01wZRu5avX1bnvfAe4\n887W1yUiIiIiIirBYIqIZozIVUwt7KliaTJjqeo4pcPPIyEwNNrE3Wu2THrGVNWViITEq45c1rpu\nPy4XTB28UN1PVzGHcAAAIABJREFUUziZVj4nCnHvsv0x0GuGkOsd/ABgdDhpfSsJpu58aH16fMN9\nz6iDOFbtesl77ulWVVBOHGHPRb3q8XYwdcopAIC5c7rhxBEC4aCvZoae17/5LQCAf+VV2WotbXjY\nDC23gynbVVepn10qJEOtpuZcaVu3tlZMjY+rz3LuXLVOREREREQ0SQymiGhGffzsw9Ljiiszx1FB\n2CSiKN1d79+uuX/SrXyuFKhVXcypOJNq+9PcJBurx2Z3vVA68MIAvszufOdYj/VCa26UZq1fd9fq\n9HjVuq3pemxfmzzWia17s4Op0dHk5upwoxC+lKqqKvHZK29R9zI2iv7No2hhz43SVVtldLBVrWbb\nEPPBVLVqqsHmz28dwk5ERERERNQGgykimlGvPWbP9LglmBLFwVQMQCLG+sF6azAVhmaqenK+0Qww\n2gzV8xWsZ47zlUVJ0DMeRJmWwErow3dcMxMKgBubqqM9epzM4/PHnlVdpQepI4rM81nB1EVnHmzu\nTVcrCQGcfbY6fuUr4UQhQiHRHDVh0zNzl6jHn/oOPD0whLbsYOqww8rXq1UVjvX1qT/brXw6mNLB\nVU+PCc+IiIiIiIgmgcEUEc2oai6M0hwpIApa7kQUIU4qpjaPNk0wlW/Js46Hk0Hpzbh4PfMaURJA\nve99wK9/bQVTsIIpmVZMCVPjlKmYmiOTgMsOuqzjihVMCWvgOWAdJ+/pmD3nmnuz5zutWKHa5RYt\nUsGUdFBPWgi/9up/Si+7ae/nZ1v19toLLexgSrftAWY2lR1MNZvAs8+a+ywLply3uIWQiIiIiIio\nBIMpIppR9pwpO6QCANdtDZuu+fNaNXtK50Hthp8ng871Dnrp0PTcOoDs8PMgAL74RTW4WwdTYZwe\nh0Ji33lVBI5jqp0AOJEJYWTTN89rV2jp9xbZFVNm4Hn6dHbYZt+b3crXbKa76ck4QiAdNJMKpTXj\nMbxQ3YPvuPCbVvBkt+JpdnBVtGOgHUw1GubegqA8mHKc8tlVREREREREBRhMEdGsqTi5YMpLZjhZ\nYZITh+mMKQCIpMQDa7eifySZa1QQUqUDxSe7g5+u8tFVSgBuW70Foa9Clj0X9aDHVQFVGioBcK1g\nSuiKqJKKKftaqQOr/LX5YCoMsxVTjUYaTDmhqphqjqhWv3HHQyUNpjxsHrTmSdk7Amp2gFS0Y6D+\nWamo13Vdc292MKXXAVZMERERERHRlDGYIqJZk9+lz/VywQzUPKZIiLRSaTwE1m4awcXXP6SutcKm\nr93wKPrHfECHR0WD0ot25SsIpiIhEPh6oLmrhrBLB551y3YrX/ocYWjmRlkhjX2tY1VMpVVV+Yqp\nolY+u2IqChAICT+pmGo4lbRdsOm42Do0nn1c3kTBlL43XRElparaCgITUgWBWtdVZ66rfvo+8JGP\nADfc0Pq6REREREREFgZTRNQxhB0aJccyjhELYSqmkpCqfzQw1ybB04/vXINfPTiQ7lQXT7SDnw5/\ndDiUCaYk4iRYEq6T7A4oUJGmle+cI5aae7eDKf181hwnu9IqbQEMAoi4TTAVBK3BVLWqniMMEUkH\n/qgKoBpuJR2w3nS8TJshmk0TJmn2jCnfz4ZN+dfTwZXjqHtL7gGNhjluNs09CwFceilw000gIiIi\nIiJqh8EUEXUMIVtnTMk4QiRkOjDcjwERRxgLrHY4fS1iBEKkwVA81VY+HWQBiIVAlIQ0QkrIKEIo\nJF531B7p/R68uNvc+wTBlN3Kl1ZMhWEaomVa+UZHgXvuye7KB6jwJ2mjk1GEQEqMDY4AABqulwZT\nxx6wBE4cIXYc4HnPU/fR22uep7vb3JvnqfBLry9dCuyRvMdGQw1b37IleRNJRVStll0H1DW6lc91\n1T92qEZERERERFSAwRQRdYxYWkPDk5DGiUJVJZVMPx/zY8g4xkhS2BMHQSbEimHa/iLpTFwxVTJj\nKhISDzytAhnpOpBRiNOO2B3/+rIDzA1bwVNhMGVVH2Va+XT4FYbmcXbFVH8/cP316lgHU3GsXi8J\nptzQRygdbNw0BEBVSXnJgPVXHL0XvDBAsOfewJ/+pB7f02Puu6/P3PuKFSpA0utf/CJwwQXquF4H\ndt8dWLdOvX4+mNLrAPDMM2YdUNeMj6t/5s8HPvc5EBERERER5TGYIqKOkamYslr57OHnG8d8OHGE\ngTEVrDy8fqhgHpW61nOd8oope46THUwlx5EQuPXRAUQQcJLKKtdz4VoteXbwNJoMIc8EXV/6Urre\n51q7+cVJi+Btt6HSTKqKfN8EU8PD5jWuvda8lh1M1etoShcn/F6tNx0vrcpya1XUggai7m4zmNx+\nzr4+c3633dRz62DqxhtN8DQ+riqifF9dPzICXHFFdn3OHPP8jz8O3H67CrFqNRVcVSrA1q3qsURE\nRERERDkMpoioc9gVU3YVlDX8PEza+iKh1hv1ZnqtiGOE0kmDqUXzuluDqfzwcztI+sMf0mqnUEg4\ncYRQSkgp0iBLtxQCyARTaaueXTFl6RWtFVMAsGzDGnWgZzQBwNCQeeA996j7bDYzg8fF+BiOfmYV\nzl51o3q4a+ZKVTwXXX4DYa3LBFB2S2Bvr6mYmj9f3W930pb4xS9mK6LseVKACpnK1h97TB1v3GiC\nKcdR/+jHExERERERWdyJLyEi2r7++dQDsGGwYP5QvpoJah5TJASk1WIn4whRcm1ktfI5UZgJsYTj\ntFZJtWvle/RRa8aUTKu1XCuYyoROmWAqMK+R220QACqR1dYXtwZXaDSKK6YAE+zoiqkoghgbQ5fX\nZx6uW/k8DxXPQZffQNA1xwRCdqA2Z456rmpVBVJRlG31syui8sHUZNaFMK18gNnZj4iIiIiIKIcV\nU0Q0497/sgPxyXOOaDkvpEAEgUfWD1qtfBFC4aRhjt6VL0wqpoKgNcRKd7rTFVH2bnftduWzjnVL\nYCQkpB6obg1HT58vkVZM2c9nGRs1QZwsC6b0vbULpoQANm9WLy/MV3jT8eCGoQqmXImuoIGgVjOB\nkH1P++9vZkVVq9nwDgD23lv9XLWqOHiaP19VXT34YPF6EKjnePBB9Wf7PoiIJqkZRLj0+lUYHPMn\nvpiIiIh2WgymiKhjSCEQSon/u3+9aeWLVDudE0dAHCcVUSqAAoDIN+1taZCkq4N08JSsF86bsium\n4jgTTDlRiFBKuE5JxVTRrntBUBhMdccmxNpj67Otb77RMPf59a9n1+xg6o9/BBYvBpC0Kupbcdy0\nYmq/Sy/E8559HH/Z1FQtgPq9aXvtpX5Wq+qfOM6uL1+ufg4PFwdPngcsWVK+7vvqOXTAxoopIpqG\n3z3Uj6//4QkcefH/YaQRTPwAIiIi2ikxmCKijiGFqVSyq6B0ZZCTBk9ROmMqDIL0WjcK1bDyqKRi\nymr7S8+XBlOmlU+KiVv5FlSEOVcQTPXE5trnb3i09c2PjJhgKs911bqeM5Xo7amlx1e+6wS85tBF\ngOdh0Y+vBAD0h64JhC6+2Oyg19Vlfupg6sgj1bnly819BEFx8KTvKb9+1FHmcfYOfQymiGgaeqpO\nevyTu9bO4p0QERHRjsRgiog6hhTCzHaCCnpkMtAcUEPDw2T9vBP3RQSByGrlk5GaPSWRVADp4Mlu\n5csPP7d35bOCKbsyyymbMVXUyuf7hcHUcQ/d3v7NX3cdcNNNJR+MVEPH77sve94KsvY58xTM/d63\nANeFqKrAatyzAqGjjgIuuUQd6xlRumIKABYsAA4/HHjhC7NBnh08vetdabVWYTD1gQ+Yz4DBFBFN\n03gzRCMI4Yfmu7S74rR5BBEREe3MGEwRUUdRM6LMDCm7OkrGari5E0X4l9MORCglwpYZU+ZxafBU\nNPx8EhVTulprUsPPw+S4JJialPPOKz7vlPxCZldYrU2qCTwPsqbConHPmu1UrZrWQ10xVauptjzH\nURVVnmdCJUC9P88z70uv69cuWtfHdjBlP46IaAKHfPSXOP0Lf8R403yXBlHc5hFERES0M2MwRUQd\nQwqBbx/9Sty5x2GIHBMwpa18ybwpgQhSCET5YCqKrEDLqpgSorWtzx5+HrYOLm8Zfj5BxZSng6mt\nW7d/dZAs+arWQVD+XDJXKu7rbR9MdXUBlYp6H3FswiQ7mKpU1HGzmQ2bHKd4XT9Or+fuiYhoMp7c\nOIrhugm0V/70Plx37/pZvCMiIiLaURhMEVHHkBL41F+9Fb854DiESSufk7TnAWbelBtFcKRABIlH\nczv4pddGuSAp39Znz5vSAUp+xlTy2o4U2SBLsyqmnNAazDtTwdTcua3nKhXgWTVc/dFFewNnn63O\nV6smHNLPd9hhxRVRrJgiolnyxMBIevzhn96nvn8Tn/2/h2fjloiIiGgHYzBFRB1DCvMLSAhTMfX6\nF61Ij0PpwIlCOMkOfk4UpkGKE0cIhAqelnS7pkoKUNeEYXaHPh1S6dDEqphSM6ZUS6HUrXy6NVCz\n7te1g6kpqs9b0P6CsmDqjjtaz+kKJgAj0gPGx9Uf7Iqp+fPVz7/9WxMmNZvq2BomjzBsDZ502KQ/\nT/sz1Nfqqiv92etgKgyBX/4SePzx9u+XiJ6zbnh4IPPn0GrhGxjhrDoiIqJdEYMpIuoYwg6mpGnJ\n6+1Ww7qveusxCIWEE0WQUqQ75wVpdVWYVkz97F3Ht1ZM2aGLfWwHU0mYEiXBVCQEuj3HzKrSYcuV\nVwKnnqqOv/99jM61wqUpzpiKdGtdmaKWPQDYsqX1nA6iADRi83miWgWeeEIdd3ern47TWuXk++p9\nCtFaEaXDpigyFVFCtFZa5SumKhV1zveBM84AfvSj9u+XiJ6z7IHn2kVnHQYAOGTZnJm+HSIiIpoB\nDKaIqGNYMYrVyhdCJoHHQYu7VcVUnJ0D1bTa/gJrB79MkKRb+eyKKV2JpIMpu5UPEhIRYiHRU3Vb\nW/ns6qkTT0TsWIPIp6jLneCr2KqCmtCjj6aHdWENTa9WgcsvT14wCcKKWvHsiqiyVj39OeZb9fIV\nU/l1u0WQiKjASL31++HcF6/ASQcsgj/djSWIiIioozGYIqKOYY0SQWDtric9FbCIKDTte4Cqnooj\nNJPKIMcelB5bu+4BxTOm2rTyRUKoGVNCoKdqVUzZwZR+bqlCrFSc2z3qJS9p+75F2W582uLF7ddL\n6JAOQLbqyg6QyuZCFQVTdrA0UfDkuubz1Ov2Z09EVGDLWHajBM9R3++uFAhC7sxHRES0K2IwRUQd\nw54xNaQzjyiEkwQeIooyFVPdPTW4YZBWTOkZVAAg7UAEKK6Y0qGLHlZuB1NSpjOmuj1HhU12MJVr\nE5Q6jNprr8zsKQDAG9/Y/o3vsYc5rlZb1+1Qqbe3+Dne8paWU3YwpQM7AMW77pUFU2W77uVb9YrW\nAfM5N5vqc3EcDkInolJX3rEm8+f53eo7yHVkYZsfERER7fwYTBFRx7CDqS1687cwgPRMyKFmTKlK\npdj14EUhvnP7WgCqfS+tmIrC4mDKrtppF0wJCRknrXxe8lWZr5iyjmUc4w8rXoBvX/l7xPmKqb4+\nFT7VaqadzuZabYAf+EDruhDmcXpwed5FF7WcCqR53gc2mtlTpbvu6RlT+pqinfaKri2rmNLX2v87\n2O2CREQF9l7YnR73VNV3iecIBBErpoiIiHZFDKaIqCM1k/lPrlUxhTBEIB24OpjyPLhRgGvu3QAA\nkHGYVky5ZcFUUShTGEyp+VWREGg2kyClTSufgxiRkPjYzx9Eoxngnt0OxJo9D1Drc+YAr32ter3k\n9et77GXerOcBV1yhjlesaP0wpATe8AZ1/NKXmvPnnmuO991X/bRCrsDazS+UEjjoIHM/L385sHRp\n6+eR33Wv6PPK77qnq6fKKqaKKrGIiHKCMEIcA8euMJtJ1DxT+flY/wiG66y4JCIi2tUwmCKijhFY\ng22byeBuLwrg1ky7ma5kUose3DCEL/W1obWbX9RaqWPPOSpq5dM7zgHpjn+hkBgZT2aelFVMOU4S\nTKmKLxFHuGXvI/DVN30oXU9nVCWvHyxYiJv2PlKtuy7w1rcC996bDZ6uukr9FAKYNw94+GHgG98w\n6x//ePYDfOYZYMMG83laFVNN6QJ33AGsXg3svjtw3XXqtezPoyhA0ut6Jz6gtZVPv798RZW+1m57\nZDBFRCXGfRV2z+0y7ctdScXq9fep77Yv/OZRPD4wgtUbR2f+BomIiGiHYDBFRB3Dtwbb1pFUPoUh\nXL2LXKORqZiC58GLAjRd9UtMJfDNLCVd4aODqWpVBVDtKqZ8P60CipOKqVgI7L8oaSvJVUnlW/l0\nMKWOJUbGG9lrpUwHo4tGw8ylcl0VPh1+eLatT4dCuvLpwAOzM6gca7g5ACxfDixcaD5Pa6fAsRCq\nUmrvvbOP0c+tg6V8MKXX89VT9rX6c8lXVOlj+3NjMEVEJcab6ntinhVMvfoFu2ev8UOc+tkb8dLP\n/B7rB8dBREREOz8GU0TUMezBto3k68mLAng9STBVryOUjhpsDqiKqShEw1EVVZXQx5lH76nWwlwr\nX60G1Ovth583m2nY9Lt/PSUNmF55+DK1Xjb8PGnli9OdBGNEEOhKdpPKBFMjIwCArpFBLO1LQiY7\njLKPdSiUH6aeXy8RWut+2dd9vmLKnjFlV0zZwVR+UHpRm2S+YsoOpjj8nIgK6IqpZXNr6bm3HJ8N\n0/uHGunxl3/32MzcGBEREe1Q7sSXEBHNjEwwJcysKGFVTIVCwo2tiqkRUzFVDZo4/Yhkh7t8xVSt\npgKodsPPfT8Nmxbf+6d0xpTQlU1tZkxJqGsRx3CSoeki3/YnZXo/UkrstzB5X0VVUvZxWQCVr5jK\nqbumuipESbiVb20sa+UrqoiaTjDF4edEVGIsqZjqrbr419MOwsHL+iCSYH5BTwWbR5v4zapn0+tD\nDkMnIiLaJbBiiog6ht3K14gFIgi4YWDa15KKKb0rn0gqpkLpIBASlTBobdWzW/nsiqmiYKrRMMHT\nN74BGUeq6qhkJ77MjKmkukpAvYdQWsGUPWPKHsZut/Jp27FiquGYdpgwv1OgNtkZU+1a+aZaMcVg\niogK6IqproqD95y8P049ZGm69vGzn9dyPXfpIyIi2jUwmCKijhFYFVN+GMF3HHhRqKqdAKBeR9Px\nUA0DII4hKmpXPgBouh6qQdOEWI1GcStffl0fAyo80dcnM6BiTCKYsmZM6blRkRBw9TdsQcVU5vFF\nFVOeZwKpaVZMNdxKelxaWbAjWvmKhp+zlY+IJqBnTHVXWgv6zzxit5Zzv37wWYw2GHQTERHt7BhM\nEVHHaFoVU80wwpPzd8eJx+6fCZMaSdsemk2ISgVeEng0HQ+V0Dchlg6e4liFInr4ebtgCgDGx9Pn\nl1HSnlcWTFmtfHGk2vf0joGRkHjPSftkHydldmB4u4opfe/AtCumAmv4+aSCqaLd8/LrQOugdB08\nle3gZwdT9msQEVl0MNXltQ/dtcFxH+//wT078paIiIhoBjCYIqKOYc+YagYRTn/bV9D8lw9mBpan\n7Wn1OqTrpkFQIB04cZRp+8sMOndd89Nxsut2BU+zmf6UKGjlK9mVryqRqZiKhcCyvqRiqayVz271\ny3PdiYOpCSqmbJMKpvJVXL7fug6Y4Ml+L0XrgHmOos+NiMii/x3guSXfeQXuemrLjrodIiIimiEM\npoioYwS5YAoAXClM4BGG6aBzNBqQngs3mTcVCwEnirIVUfagcztU0tVTel2HUUCmlW+PuTXsvqCn\ndK5UGjYBcBHjmH0WQqQVUwWVVlICr3+9Ord8uanUqpkdqNDTo35++tNmvWqGmAMATjhBPU9ZxdSF\nFwJLlmROlQZT1mfbEkzpzw3IvNeWiin7/dnrgHmOos+QiMiiZ0a5E1SD2spyeyIiItp5MJgioo5h\nDz/XwZQjRaZqp+lkgyldMRXqNjo7mLIrpuyqHR1MFVVMWcHU3nOr5cGUDrqsMEY4Tlox5ekKLftx\nUgJHHgkccQQwd65pG+zuNq+vW/jOP9+s610JtZtuAr7//fKKqYsvBp59NnNqUsPP7fend8/LB1fJ\ne83srldUMWXv4FdUMRXHwNatqnKNiAgmQHdl+7Tp5IMWp8ddlclXjhIREVFnYjBFRB3Dtyppmkn1\nlOtkg6l/PetIdVyvp7vyAUAoHLhxlBmUXjrnSA9C1+v5iikh1LoOk9oNP88EUzINps56wR5tr4WU\nJpTJB09aWTClTaGqYFKtfHaVl+Ooz00IE8Lp0oQoMuv62qJ1oPWz168xNgbMnw986UuTfg9EtGvT\nFVPOBMHUa4/ZMz0+YEnfDr0nIiIi2vEYTBFRx7CLejKtfNZQ7UULetVxEEC4LpwkmIqkhCfibKVO\nPpjKVwOtW6f+bFdM6TAmCEzA0i6Ysu5NOmb4uXTL2/7SYx082a18tonWJ5gx9ZsPvCQ9nvSMKUD9\nD5GfgVW0bh+XrevPyK6YiuPstUREAMLI+guJNjzH/OdrxO8QIiKind6EwZQQ4gohRL8Q4v7c+X8S\nQjwshHhACPEp6/xKIcRjydpp1vnTk3OPCSE+bJ3fRwhxuxDiUSHED4QQFRDRc97373waAOBIWdxO\nlrTn6YqpSAh4QMs6gNYZU1KqKqmzzlJ/LpoxFYatgVY+mLJ3rUtb+dS6yFcU2cGUfh/XXAO88Y3A\n0qXFH8Jb3wqcfjrw4Q8XrwsBnHce8JvfFC7vv6QP9U9+Ghed+o6pB1P5eVr5Vj37eDLrun2vqHqM\niAiTr5iyg6uGz+8QIiKind1kKqa+DeB0+4QQ4mQAZwM4Io7jwwB8Jjl/KIA3ADgsecx/CiEcIYQD\n4CsAzgBwKIA3JtcCwCcBfD6O4wMAbAHwtm19U0S063BEtpUvU4njZmdMVUTUsp55nA6mHAfo7zcv\nkp8xpdvNNm9W7WZlu/Lp9eT1pDQzpoTjqHXAPEc+rDn+eODKK8tb8ubPB37xC2C33co/oG9+Ezj1\n1NLl8J/fh28dc/bEwVRRRVe7YCrftjdRMJV/XgZTRJQTTjD8fO+Fah6fZ603Q36HEBER7ewmDKbi\nOP4DgM250+8GcFkcx43kGv0b3tkAvh/HcSOO4ycBPAbghck/j8Vx/EQcx00A3wdwthBCADgFwI+T\nx38HwKu28T0R0S7Eyc2YylQdua5VMSVRQdyyDqB4APeGDeZF7IqpZlOFLmEIDAwAixeX78rX36/W\nAWvGlK6Ykib80s9RFNzsYLryYNLDzwETPE2mYqqola8ouLJfg8EU0S4ljmM8uXF0m59Hb4BRVjF1\n7IoF6frv/+WlmN/toRGE2/y6RERENLum+5vRgQBOSlrwbhRCHJuc3x3A09Z1a5NzZecXAtgax3GQ\nO19ICHG+EOJPQog/DQwMTPPWiWhn4kpRXn3junCSYCMSEsv6qtkQxA6mpATWrwc+8Ql1zeCgeRE7\nmLrmGhW2hCEwMgL09WXDk3e9S/2U0qwn9yMdB1++9pNq2XHUuuuqGVGzFEzp3a38oCSYKtt1T8+C\n0te0q4hqtx7Hra+RD66IaKf2u4f6cfJnfo9f3Ld+m54nnTFVEkxd+IpDceErDsVRe8/DikU9OH7f\nhek8wtS//ZuqJF23DvjCF1q/Y26/HbjqKvV9dNllwJYt2fUNG9T5OAa+/W3ggQey674P/Pu/A5s2\nAV/9KvCZz2zDOyYiIiIAcLfhcfMBHA/gWAA/FELsC6DovyRiFAdgcZvrC8Vx/HUAXweAY445htMu\niZ4DHCkAUVIxZc2YWragB30Lu4pDLHsW1IUXAvvso3aF0+xgSj93s6meo1o1v9g89RSwcaM6lhJo\nNIB589LXE47EC59OfomRQq1Xq+Z+ZiOYciS6PAcjDb/4gmXLgM99Dnj+89X7s+/PrhSzA6ai9xLH\n2bCpXUhlB1McXEy001s/qHYYvejnD+KMw9u0Hk9gohlTc7s8vO3EfdI/V1yJhh1MBQHw+c+r+Xz3\n3AN8+cvAAQcAc+aoWX033KBaqAF1fuVK4I9/BL77XdUS/cMfAq97HfCXv6iff//36to4Bs4+G3jT\nm9Rfalxwgfp+X7UK+O1vgX/5F+Dcc9W/V370o2m/fyIioueq6f5mtBbAT2PlDgARgEXJ+T2t6/YA\nsK7N+Y0A5gkh3Nx5IiIAuV35CmZMOUnrXKXiQkRhccWU/ThA/fKgd7wDsjOmNL1uB1N2mBRFKryy\ngifpOJBJtt7zs2ta1gurk2ZAX83F0HhQvLhgAfD+9wMHHVQ+Y2oqwZRuAZxMMGW3CxJRx7rn6a3o\nH6qXrleSXfI2tLkm7+nNY4hzwXQY6hlT7Yefa1VXZoefDw+r7/cDDgCGhtS5gQHgbW8DbrkFeOKJ\n1id54gng6qtVGHXZZcCjj+qbMdfEMfCznwFveIOqvAXUd9chh6jKqShSlVePPz6p+yYiIqKs6f5m\ndA3UbCgIIQ4EUIEKmX4G4A1CiKoQYh8ABwC4A8CdAA5IduCrQA1I/1ms/ovkBgDnJM97LoBrp/tm\niGjX49jBVD7ccV24kd75zi0MrgCoAeM2KbPB1J13tr6wXv/Od4C/+ZvkNaxwy3FMRdTAALB5M6Rr\n1uctXZitmNLthPre7OfawfpqLobLKqZsZcFUUdhkV0fZAVPRDKl8JVXRzC4i6kj9w3W86is344WX\n/hZ+2aBxK0cq3WjBsnGkgZM+dQMu+vmDmfNjfghHigl35dMqrswOP9ff211d5ru30bBuLmw9ttcB\ns5tqYIX5o6Ot654HdKth7KjXVVu3DsOIiIhoSiYMpoQQVwG4FcBBQoi1Qoi3AbgCwL5CiPuhBpmf\nm1RPPQDghwAeBPBLAO+J4zhMZkj9I4BfAVgF4IfJtQDwIQAfEEI8BjVz6pvb9y0S0c7MlbK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wChdBBEKpiSlQqkNVeqEk1QMVV0zj5vt/TlK6Z026Dvq/MMpoiIiCaNrXxEtOuRsjyU0vbeu3UX\nPduSJe1nRvX1mRlXHU7vyvfZXz/S/sLp7MpX1spX1vbHiimijvf4wAj2X9Lbcr6ola+sMkpXaubZ\nO/5tEx1MJaFR4LhwoxCBdNJ14XnwrGDKDUsqonTwZAdTRcf6Ma6rwih7xlSlYq5xnGz1FREREbXF\nYIqIaBenh5+vWj/U0naT0S5syldM5cOmfMVU0Q5++Yopzpgi6jhxHOOZrePYc0FXy5pXEOavPOOQ\nwufRlZpzatnKqTOP2G073CVaKqZ86cKJIxNMNZuA52XCKC/cxoop/XOiiim28hEREU0JW/mIqOP8\n5N0vxvrB8dm+jV2G/gURALaON7Gkr1Z8YVnY1K5iKo7bV0zpdftxbOUj6lh+GCOOW3fbA7KtfJ8+\n5wgs6qvi0OVzCp9nbreHS179PBy3zwL89ef+kJ6Pom34//yvfgXMmQMcd5z6HrEqpnyrYqqv5qqA\nqK8Pv/yH44AvqId7Za18erj5ZFr54thUTNkzpnQw1WyadSIiIpoUBlNE1HGO3ns+gPmzfRu7DHuL\n96FxvzyYalfZVBQw6WN7+Hm7HfzKQiwi6hh+qP4/WTTo3LUqpl57zJ4TPtebj9sbcS58DrcljP7A\nB9SMwCuvTG7IzQ4/j9Tg8UtefThwnWq127OvYu6/rJVPHxeFUfnzum1PV0bZ5/S1lcpOM4OQiIio\nE7CVj4hoF2fPhQnb5UBlM6byrXxFg9C3ZVA6EXUMHUxV3IJgqmD4+USEEPiftx+HVz1/OYDidsBJ\n0zve6aAoVzHlRBH6emo468jlLTOoAMCLre8bXSUFTDxjKh9Y6eBJCDNPym7lq1TUvYYh8JWvAF/6\n0vTfMxER0XMAK6aIiHZxdjAVo021QkmVUywE+gfH8eAjAzgZQByGuPruZ/Aa+9p8FZRdXVXWyscZ\nU0QdZ+0W1UZdNOjcLTg3GSfsvwhH7jkPu8/vwqteULDL6WTpuU52MGXNmHLjEMI1w8/tdQCZQeht\nd92zz+WPfR+oVoFNm8w9JPOsMuv6+OqrgfFx4L3vne67JiIi2uWxYoqIaBdn/4LZNgcqqHLqHxrH\nn9YM4tENQ3j7f98NABgeb+LHd69T15ZVQRVVV+VbBDljiqjjrPzpfQCAO57c3LImxPSCKQDorbr4\n19MOLqzEmjQ910nPb3LdTMWUjCK4OiDSu+bZFVPRBLvyTSaYajaxyY8R63NdXSp40q18uqJKH+t1\nIiIiKsVgiohoFyfFNCqmkuOnN44iFoCMY0TJ88g4Riza7OBXNmOqqEWQFVNEHaURqKoiv23f7ywp\nauVLZ0y52H9hF2S7iim7la+oVa8smLLa/lat3oib1wxj85YR9brVGp5Zvzk7/FwHU40GgykiIqJJ\nYDBFRLSLy7TyxUAziPDRa+9H/1A9e2FBmOQiRgwBkfzU67F+yvzw83atfEVD1RlMEXUUPeA8CDuw\nmlG38umKKc9Lj33HgRtHah0wc5/0tStX4o79jjLPZe+aFyYtfmVzpc4/H7j8cgBAfWwcvuMiqDcQ\nRjHW1oE7Vz1j2vdYMUVERDRlDKaIiHZxjrCHn8f4yNX34bu3PoVPXLcqe2HBrnsSasaUSHbUi4VA\nHIaIRK49j618RLuEM563DADw4TMOnuU7KaBb+XRoZM2bCq1d+QCo8/Y8qhNPxFDvXPNcE1VM5aun\n5qrHVkIfDceD6zdx3KW/Rd2toBY0ETgqEHv06Y34j5vXqMexYoqIaNKiKEYY8b8Ln6sYTBER7eKk\nVTH1n79/DD+6ay2Agu3gC8IkGVvBFIBYSERhlLb1bdPwc33caAAf+xhw883b9X0T0dS5yffCHvO7\nZ/lOCuhWPrtiKh1+7sCJIxNM5SumPA+e3co8UcWUfTw2llZEheN1NB0PXuBj40gDda+CLr+Be/pV\n+PSH+9bikS3JY3UwNTa2Xd4+EdGuauNIA/tecD32u+D62b4VmiUMpoiInkNutwYa91ad7GLBjCnE\nKoSSaTAlEIcRIuSuzbfnCWGO8y2C+WAqCICLLgJuuWVHvGUimoIgmS3lOtMfdL7D6IHmdsVUEjCF\n+WAqXzHlutkZU3ZFVNE5O5iyhpsH43XU3Qoqvpo7VXerqAVN9CeXO34Tda9qHtfdrX6yOpSIqNRr\nv3rrbN8CzTJ3tm+AiIhmTmjNjSmtmLKqnMIgRAQJATuYCgF7xlS+0kr/cpcPpspmTNmvS0Szyk/a\nKFxZHExd/Q8vxuK+6kzektF2+LmDKmIzY0oPP7cqplwk3zGOg+Z4A05PL5xRNcQc1aoZcu44Jpjq\n7W2pmGq4VdT8BhDHGPeqWDA2hK2h+rwqoY+6l8yYGh8HenrUffu+mT1FRESpOI7x5MbR2b4NmmWs\nmCIieg6JrL+1b/n7+4KKqTAMEQsBmVQaREIiiuPWGVP2sT1vyl4vCqny60Q0q/wwgucICFEcTL1g\nr/mz1+aXb+VLKqZiKXHZOUdiWY+XbeVrqZhKvpt6e7FpcBQDwgqKenuzx76vwvNcMDWwcSgNnqqh\nj3G3ii6/ga2B+ryCsQbqrhVMdSef1dgYcPvtrAwlIsoZbgSZP3fkrrCJ0UaAxwdGZvs2dkkMpoiI\nnkPsMKqls6QomAoiREJCxrHafE/q4efql7CxerP1cflWvXbBFCumiDpKEEat1ZSdQodNeph4Vxfg\n+xCeh9cfuxeEHn4ehmZHPLtiKlbfXX6tC14YYEvXHPPcfX3ZY/143YpXqwEARjZsTIOnatDEuFdF\nLWigobcq7e9H3c218gEqmPrQh4C3v31HfTpERDulS/43uxnPwxuGZ+lOJvbO792FUz97I2K2Z293\nHfpfHkREtCO03e2kIECKAvWLnIhjrFjYg1hINWMqqZjaMtIwjwvD9sGUXrdfg8EUUUfxw7i0jW/W\n6Yqp0aTlo7fXDDm31/Ww8Z6eTNufC1Xtua4BeGGAgZ752efW5sxRbX09PSqk2ro1rah63ac+gFGv\nS718cww9C+djTmMUjUB9f/3d3ddhrKJCLIyMmMBr61bgxhuBVauANWu2+0dDRLSzun/dYObP77ny\nz7N0JxO76bGNAIDfPdQ/y3ey62EwRUT0HGL/BU+cb+Yr2EnPT4IpGUeoulLNmIrC9JGBH5RXTBXN\nkCoahM5giqhj+J1WMaW/F+JYHbuuCnwAEzzpuVK6ospet9r+vDhCKB287G2XY2tXHzZ1z0WkW/hG\nrNaM3XYz86GWLwfWrcu0+m3ungsAWDA2hJP+6gjMq48gHDXzUTYl6xgYUI8H1HNo/f3AX/4C3HPP\nNn88REQ70pEX/R++dfOTO/Q1dp/XlfnzaCMsubJzvO07f8ITbOnbrjrovzyIiGhHs8Oob928OrtY\nVDEVhYikAxnHiGOgGQHDow3EQq/HZqaLfpyuPNAzpPLrgKmeiuNsIEZE07Z1rIlzLr8FT28em/Zz\nBGG8/YMpe4e7whcNyv//f/zxwOtfnx1MrkMgO5jyffU8+Yoqq2JKBAECIdF0PXT7dYxVahg57Ei1\n7lr7AS1dqu6nqwtYvBjYtCnT6jfcq4OpQThLl6iHbzE7ng7WehEL8f+zd95hclXlH//eNjPbN40k\npJBCSOhBEjoBNXRpgiCiiAiI/EQBRUVBBaUoiggIAhFBiiAoIjVIhwChpEJIQnohm7672Z122++P\nc8+95945d3ZmdoDd7Pt5njx79p7bZjZz5p7v+b7vC2zaBAwcyDZu3hycv6YGuOgi4JJLir8vBEEQ\nnyGu66ItY+KqJxZ8otcZN7g+9HvO6vnCFAB84Q+vfNa3sF1BwhRBEEQf4MSJbNW+WCQfxo9nE6kT\nTwxyTJmO75hatH4bHEXFa4s3+jmmXFl4npj8vJQcU+K+BEFUzBNzP8a7K7fi9leWVnwO03GgaxWG\n8h1/PPDVr4a3rVjBEoc/8ABwzz3AwQcXHmcYwNe/DnzwATByJNDSEvR9/DHw6qtMzAGA224DHnmE\ntRsbA8EnkWBhckOHAm1eWEhDQ9Dfrx8GbV2PDfX9ANdFQy6NjkQtnv/2Zaz//PODa44dy34OH87E\nr87OkDCl19UBAFJWHslmL09VOhADXUWFlUgy1xV3WnV2Bue1rHAVQIIgiB5I0fQPVSRnOqhLaLh4\n6jgAwP6jB3wq162Ek7znaaL6kDBFEATRBzh98ggA4ap8Beg6MGAAmzB5wpNj23AgVuVToLkOcwMA\ncG2J8MQdU8VC+Ww76AdImCKIahBTSa8cTNtFolLHVEsL0N4e3rbUE8luvhn41rdYVbo2IZ8IHy/+\n8Q+2z+rVwGmnMZHJcYD168NCVUsL8NRTbMzo14/1c4fUtm1s2003sd8HDWL76zrQvz9G59uwtnEH\n1Jg5pKw8ttQ2wuHn/tKX2E9dB0aw8RJ33x0IU7oOHHww5o/fF4l6JpIlbBOKIDz9e+rXkNcTqEto\nsIxEkKfK68eNN7K2ZbFk6tlsZe8zQRDEp4D1KQlTluNC11RcPHUXDG1KobnW+FSuWwmU8vyTQ+96\nF4IgCKK3o8ATkiLfqJbtQJdNQv2qfLZflQ8AHFWF6jpwvPOta01jbJ0gPGla8ap80bA+EqYIokfB\nxoQKBa58ngnbAHDcccAZZwSOIR5SBwCf/zwTkF58EZg/P9jOk5i/9hrwta8Vz8HU2BiE3zU2BpX6\nHnyQ5XACgD/8AXjsMWDIEOCKKzB0wWzM2eUg9M8w8WxrqhFJHoI3aBAbh5JJvwIfslkmLGUyTEAz\nDKhWB2oaWKW9RsX2had3P1iN2k4bmm2hJqHDNJKo4ccDLIcVf32mGXZMTZ/ORMUjjyzpbSYIgvg0\n+LQcUyy3IfveqUvq6MxZn8p1K8G0g2fVQQ3Jz/BOtj/IMUUQBNEHiDNSZK0YMYgLUw5zR+1QxyZU\njqJCcV2/Kt9DM1dic9qbcMrEJh6yJ/aL7WjYH0EQZeG6bsllqyde/Rz+8Nyigu05y8av/vsBNnfk\nYNoOdLXCx8NcjoXUbdsGPP008I1vBA4qMc/U7NlMlAKA/fcPtotuq64Sg7e2Bu1Bg4L2BqFS0mOP\nsZ8tLX5y87SRxMWvP8gul6pDom0r22fAADZQJhKBuJbLAbVMhMq2dyCvalAtE6l6tu3bk3f0+2vN\nHCxVg+Y66JfSkNcMdnwqxc6bToeFKdEx9ZvfANdfX/z1EgRBfMqIjqn317Zh/pq2InvHs749iz1/\nNR2zV22V9tuOC00NhKmOHixM5YXn5ordxYQUejcJgiD6AGqMMpXJxySY9CamlueYqjW8ZOdQoDlB\nKJ/quui0vAcXWfLzYqF8URHL7h3JLgmiJzH5mhdwyG9fAgBMe21Z7H6t6Txa0yZueXFJQd+z77fg\nnjdW4LfPLoRpuzD0Ch8P83km7Kxbx34/+uhAfDn1VJZHCgCeey44RsjNFHJVNTcHbe60jEPMW/Wn\nP8n32X13AExAOnDVPABAR6IWifZWJhw1NrKfhhF2THki1Tf/8jpeX9EGM5fHh1uYyDa2Qff7DduC\nqTEH18CkgqxmsOO52JXPFwpT3DHF+wmCIHoQomPqS7e8juNvfb2i88xauRXbshb+9MJH0n7Tdv0F\nkfqkhtc+2oRRP30K3/rb2xVd75MkbwfvydrWDNL5niui9TZImCIIgugDxDqmzBgxiOeQsmw4igLF\nE5BslYX1cWFKcR0oWkwoX7EcU44TzjElHkcQRMls6shhbSsLY1u5mYk8MgPV4vXxZa1zJvvs/fPd\nNZi5fDMMtcJQvlyOCTVcjDr33KB90UWsf8IE4Igj5Mdv3Bi0xSp5XQlTmQwwZQprjx8fbJ86NWgP\nGwYAqMtnsLaROaw6EzUwOjuYKKWqQFMTcOyx+OvbawEAWze3+cLTsrVbYGk6DNvCyk5v3BSEq4Rt\nwlLZfe47rB7trgor7YUX8rC9aChfJtJPEATRg7Cq9FzWVfpDSyi6UZcIxv6XFm2MO6RL1rVlMPHq\n57CoZVvF55CRj1QMPHPaTKzY1FnVa/RVSJgiCILoA8Q9E6S7ckw5bkiYchUVKhy/Kp8CwHK9s8tc\nUGIon20X5piiUD6C+FQ47Y43AcgnCK6QzjVrOmisqTDxLHdMceeTYQTCVCoV9PPfTzyR5ZPi8KTo\nJ5bQAlsAACAASURBVJ0EjB4dPq/IPvuwfTi2HbicbBu47z7g8suDUD7AD7lL2iau+cK38a/dP49F\nO4yCkRYq7iUSgK5j+nIWUvj6+2t94SlpmzBVDbpjI6d7r6FAmGITqt0H1SKrJ5Dr8CYrMmGqpiZ4\nb5JJckwRBNHjqFaOqfYscxVpMQqV5bjQvQWR+mR1UmA//+EGtKZN/PrJBVU5H8e0XYwaUOv/PntV\nKy79Zxeh50RJkDBFEATRB4hbrVqwrjBfQM6ysTXDHiJcy4KrMAFp3A71cBQFquP4OaY0x0HeKRLK\nx8UqsT9uXxKmCKJiwnmmXDwwcyW+dtdbkv2AZ99fV7BNZGB9orKb4MnPiwlTPH+TaQJ77BF2Cm3z\nVrYffpgJN1GamtjPP/wB2HXXYHttLXDppay9zz4sZPDaa4PE6wCw994AgLsnnYB5Q3fBD7/0Q9T2\na0RCFKZ0HbAsZHRPjDJzvpBm2BYsVYfuWNAMg21Pp4V+E6YnvNerDjJGEm6nF6YoC+WrrWXHuy7r\nJ8cUQRA9DMuujjC1zROmVE98smwnlErCEnIb1gnC1J7Dmiq+Zq3BxuPXl2yq+Bwy8paD0QPr8NiF\nB/nb1mzNVPUafRUSpgiCIPoASowydcnDcwu2XfrPufjl0wsBAI5lw1ZUKI6DRy84CM31KahC8nPV\ntZFHTCifpsldUkCwnYQpgqgKs4Sksqbt4uePvY83lm5Gzip0RV5w/6zQ72KVIQDImGV8Fr/+dRay\nBwTiS1SY0nX2eef9rss++7oeCFevvAKcfnpwnGkCe+7JnE8AcOONwCOPhPuTSeCXvwRuugk46ih2\n3qFDw/c3bRrwzjvAgAFoz+QxfZdgMtFcm8DinfcCTj6ZbVi4ELjjDmQMzwWVy0QcUSo0x4HjuoGw\n5DuqLNheKF+dpjBxi+fP4o4pHp5o276Dy3ddkTBFEEQPQ+aYyscVzSlCe4Z9J3DH1JF/fBVn3PUW\nnpm/Drte+Symf7Aei9azhQnx+6gu2UUYdxGSRnVkjosfmo1v3/OO/zurIKhi4oggD+KGbTmsayNx\nqruQMEUQBNEHiAvlm7RTv4JtT81b54ekuGaehe25DppqDdTVJKC6DkxvAqY7QVhfQVU+wwAsK+yS\nEkUq3g+QMEUQ3eTeN1b67bZMkER8bQkruVc+/kHo97eXby79wkuXAqtXs7ZpFgpTYvheXD/A8jxZ\nFhsjFIXtM3w4cPHFrD8aIsiFqV/9ilXUi+Pb3wYmTQIAP1QEAB48d3+kDBXPHXkGcM01AIC3WjJY\n0W7C8txatVY+lNzcVjVojoP9RvcH6upCwpRhm7AULkwBWSMVFqby+UCYsix2PBCcg0L5CILoYVgS\nYaqzgop53DFle/bcZZs6MWd1K777wCxkIrlOV28NCmJsaK9csBdTVbSmKx9f/zPnY7ywcIPvSs5b\nDgxdLVjwveq/1Q0Z7IuQMEUQBNEHkDmm6pM69hreLNmbJTkHANtkoXw8x5Sma1Bdx3cGaI4NBxFh\nirugvLCY2FA+3g+QMEUQ3URcxZ69qtVvr+5CmJKtiP/u1L1LvzAXk7gLKio8ia5Jy5L3A2wf3g8E\nIla0nx9nmuEE6SXAQ0UA4KCdB0JTlNDE66t3voXDf/8yGvqx0D6ns9MX1ephwVI16K6Nu8+ezEIN\nhVC+hG1C8e4n4TrI6gmoWe+956F6ojDFQxX5OcgxRRBED0P2/dBZQRW6bVk25pu2g/asWXRfcZxe\ntqmz4jxXH65r99sTr/5fRecQGX350zj5thlYtqkTCa1QQtmWK/66iK4hYYogCKIPICuylTI0ZCVh\nPgD86lJu3mKOKE80UjQNquv6/bpjwxaFKTGUr5gwxcN4SJgiiKqQF8IfbOGzlPZWt2XuSADYKllJ\n3mPHxtIvHA3L1fWw8GRZYUFG1s+PE/eNClPieMHPYZSXpN3QwgOhpiosLA9Ah+ACcDR23ufmrPFF\nNQOu55iykTK0QBzTuEjvwOZt14apalBN75z8/RHfB1GAE98/giCIHoKsKl9s0ZwicDEqbzn4uFW+\nWHL07kMAADs2p0LbOypwaAHA+vZs6Pcv3zYjvhJ1ifBFn4zkPTCrlI+rL0PCFEEQRB9AkQTzpQwV\nD85chT89/1FBn+2FpKTTOWi65jumoKrQIo4pOxrKV8wxJYbykTBFEFVDzCUlhvJx8VkWkgEA6Vzh\nA3ZtogwnUlRkjgpPUceUrB8IHFFRYcqywv3iNcoUphRFwZn7j8QD5+7PTqkqfnLftDD5MdUghx6/\nd90T5HVHGN+E16a4LhwuTNk2GyMd4bXxMU98H/j7Jwr6vZzWdF46aSMIovchdUxVIBSt3MzC80zb\niQ0v54srV35pN5w4cUeMHsjCnbd14bCKY3NHeNFl1qpWfLS+o6JzRWnyKte+ctnh2G9UfwBssZfo\nHiRMEQRB9AFkuc/5l+gfn19c0McdUdu2pZFM6MGkSVWRUADLm7gZUceUKDDxyWOxUD4+QSVhiiC6\nhRjKJ84lsl4ic9nKN4CC5OiTR/VDqpyksTyUj3+WiwlTPPwuTpgSw/NkoXzRa5QpTAHANSfviYN3\nHshOqSp+zpOc8P615dk2XRizNMWFrajQHLnwrroOVO9+NMeGpWpQ7IjwLhOm+Dm2k/Fv4tX/wwm3\nvv5Z3wZBEFVAtqBRrmPqjaWbsLCFJTbP2y5umL7I7zvn4NF++9cn7QGALYz86av74MdHjQcQ5Kcq\nF5kbWLZNZN6aVlz3zIeRKreFNKTY+L3TgDr884IDsdOAWtRUKdl6X4beQYIgiD6ATJiqEVZ3/jN7\nbajP8iaSHeksEqIwpWkY3pz0k6NrxUL5ZMnPxTYlPyeIqhFXKYmHLpiWi6QePPbxlXBRkPndKXvh\nkQsOiq3iKYULT2L+p7jwPB7CFu0X21xs4sfF5ZgaPhzYfffS71OCpirC+xBMtrZ6LjLNsf0xK5M1\nYasaalQ3fD++MOVizGAWAqk6NhxFhcLPyZ2konNMbItO0+2AjzZUx5VAEMRni8wx1Z4pz8H0/to2\nv21aji9SAYHzKKGpGNZcEzqu3hN/ShWmXNfFs++3+I7NLZ2FItT9b60s2MbJ5G2ccOsM3PHKMlxw\n/3toacvG7juif23o99qEDpseYbsNCVMEQRB9gLhQPs7FD88J9XHHVGdHFsmE4FpQVYztV4M5Vx8D\ngOWYenbBBtZn28VD+WwbRXNMbUcTM4L4tOECkxZJKPfCh+zzaToOpu46GPuPZmEHc1ZvDR137zn7\n4bTJI8q/cFSYiopJslA+25bvGw1xkwlXvH399cATT5R/vwKaqvoTL+4sAwBT4bmigvBjVvRBhR69\nd0/EO3HPwTjn8HGsy/YcU30wlI8giO0HS5I36UNBWCoFMZm5GVFv6pIaHjxvf7x35dSC4xpSTLTq\nKDGp+EuLNuCC+9/D7a8sxauLN2Jr2sTxe++IyaOC/IrPLVgfe7zoppr+wXr87LH5mLFkk3Tfrx+w\nU+h3TYWfr5CoHBKmCIIg+gDFQvlk8BxTHeksjLoaIJNhVbdUFYrroL4mAVdlYS3reDGpTIYJTOk0\nMGsWa1sWkEoF/XyCu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chdNCl4NUL9xHYVHVMD6+XhxgoU6J6wxHNFKbIcU0VC+RzRMSXe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\nzYQQUwKBQCDYprARIoOanycIGyCm+nxFQAw06QFU6uxDnfRnhQ/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tpkKY3vpWvc3oy6ad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Xzcsi0npvZf1pmncKd5Q7BsGbz+9blHaZlAKic937p15GdYtUdUWXfevE4vqOr8fN0mP6+W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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47ec23c9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(ground_true_df.times,ground_true_df.value, label = 'Actual')\n",
    "\n",
    "for i in range(len(test_output_times)):\n",
    "    if i%16 == 0:\n",
    "        prediction_df = pd.DataFrame()\n",
    "        prediction_df['times']= pd.to_datetime(test_output_times[i,:,:].reshape(-1),unit='s')\n",
    "        prediction_df['value']= predicted_inverted[i,:,:].reshape(-1)\n",
    "        plt.plot(prediction_df.times,prediction_df.value,'r', label='Predicted')\n",
    "        \n",
    "# plt.legend(loc='upper left')\n",
    "# plt.savefig('result/bitcoin2015to2017_close_CNN_1_tanh_leaky_result.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-12-17 06:15:00', '2017-12-17 06:20:00',\n",
       "               '2017-12-17 06:25:00', '2017-12-17 06:30:00',\n",
       "               '2017-12-17 06:35:00', '2017-12-17 06:40:00',\n",
       "               '2017-12-17 06:45:00', '2017-12-17 06:50:00',\n",
       "               '2017-12-17 06:55:00', '2017-12-17 07:00:00',\n",
       "               '2017-12-17 07:05:00', '2017-12-17 07:10:00',\n",
       "               '2017-12-17 07:15:00', '2017-12-17 07:20:00',\n",
       "               '2017-12-17 07:25:00', '2017-12-17 07:30:00'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(test_output_times[i,:,:].reshape(-1),unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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